Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Proceedings of Five Day Workshop on
FINANCIAL ECONOMETRICS
On
15th to 19th October 2019
Organised by
Research and Post Graduate Department of Commerce
Government College, Attingal
Sponsored by
Directorate of Collegiate Education
Government of Kerala
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Workshop Organising Committee
Dr. V. Manikantan Nair, Principal, Government College, Attingal
Dr. K. Pradeep Kumar, Co-ordinatror, Five day Workshop
Dr. Lt. Sunilraj N.V, Co-coordinator, Five day Workshop
Sunil S., Head of the Department of Commerce
Dr. Anitha S., Associate Professor
Dr. Sajeev H., Assistant Professor
Dr. Sarun S.G, Assistant Professor
Manikantan G., Assistant Professor
Dr. Shanimon S., Assistant Professor
Dr. Binu R., Assistant Professor
Editors
Dr. K. PRADEEP KUMAR
Dr. Lt. SUNILRAJ N.V.
January, 2020
Research and Post Graduate Department of Commerce
Government College, Attingal
ISBN: 978-81-936576-7-6
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
CONTENTS
1
2
3
4
5
6
7
8
9
10
11
12
13
REPORT OF FIVE DAY WORKSHOP ON FINANCIAL ECONOMETRICS
TECHNICAL SESSIONS I TO VIII
Dr.S. KEVIN
TIME SERIES REGRESSION
Dr. P.N. HARIKUMAR
INTRODUCTION TO GRETL
Dr. K. PRADEEP KUMAR
PERFORMANCE EVALUATION OF
ENTREPRENEURSHIP DEVELOPMENT
SCHEMES OF NATIONAL HANDICAPPED
AND FINANCE DEVELOPMENT
Dr. SHANIMON S
CORPORATION
TRENDS IN GLOBAL AQUACULTURE
PRODUCTION
PERFORMANCE EVALUATION OF SBI LIFE
INSURANCE COMPANY
ARIMA MODEL IN PREDICTING NSE NIFTY50
INDEX
36 - 44
Dr. ANITHA S.
45 - 47
ANSA S.
48 - 52
Dr. LAKSHMANAN M.P
FINANCIAL DEEPENING AND ECONOMIC
Dr. PRADEEP KUMAR N.
DEVELOPMENT OF INDIA
ANALYSIS OF TRENDS AND GROWTH OF
DIGITAL RETAIL PAYMENTS SYSTEM IN
INDIA
KILLING THE GOLDEN GOOSE- THE CASE OF
PRIVATE BUSES IN KERALA
TRENDS AND GROWTH OF TOURISM IN
KERALA
A CROSS-SECTIONAL ANALYSIS ON THE
INFLUENCE OF VARIOUS COSTS OF
AQUACULTURE ACTIVITIES ON REVENUE
FROM AQUACULTURE
4-7
8 - 27
28-32
33- 35
53 - 61
62 - 66
SUNIL S.
67 - 78
PRAGEETH P
Dr. ANZER R.N.
79 - 86
THANSIYA N.
87 - 92
Dr. K. PRADEEP KUMAR
93-96
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
REPORT OF FIVE DAY WORKSHOP ON FINANCIAL
ECONOMETRICS
MAIN THEME OF THE WORKSHOP
Financial Econometrics is selected as
the topic for the workshop basically to train
the Commerce teachers in various Universities
of Kerala as the subject is recently included in
the latest revision made in the UG and PG
curriculum of various Universities in Kerala.
In Kerala University and many other
Universities, the subject of Financial
Econometrics has included under two major
modules in the Course „Quantitative
Techniques and Financial Econometrics‟ in the
PG curriculum. As the content of the syllabus
is new to Commerce teachers, a short term
training course in the form of a workshop is
considered essential. At the same time, there is
shift in focus of data analysis towards analysis
of econometric data. Researchers are using
Econometric modeling techniques in cross
sectional data, time series data and panel data.
In practice, these models are used for many
practical situations involving measuring the
volatility, CAPM, simulation and the like.
Thus the topic of workshop is highly relevant
for Commerce and Management research nowa –days. Thus the Research and Post Graduate
Department of Commerce selected Financial
Econometrics as the main theme of the
workshop during this plan period (2019-20).
SELECTION OF CHIEF TRAINERS
As we need to impart both basic and
advanced level training in the subject with a
direct focus on two groups of participants viz.
Teachers and Research Scholars, we decided
to select Dr. S. Kevin, the former Pro-Vice
Chancellor of University of Kerala and
Professor of Commerce to impart the
foundation training and Dr. Vijayamohanan
Pillai, Professor, Centre for Development
Studies to impart advanced training in the
selected topic. Both trainers are well
recognized and handling the subject for many
years. Thus the Workshop was scheduled from
15th the 19th October, 2019 as per their
convenience. Frequent discussions with these
two subject experts enables us to locate the
major modules to be included in the workshop
in a sequence from simple to complex. Thus
the basic modules were assigned to Dr. S.
Kevin and the advanced modules were
assigned to Dr. Vijayamohanan Pillai N.
PLAN FUND ALLOCATION
The College Council meeting held on
30 July, 2019 allocates an amount of Rs.
65000/ (Rupees Sixty Five Thousand Only)
from the Plan fund sanctioned for FDP by the
Directorate of Collegiate Education for the
conduct of Workshop by the Research and
Post Graduate Department of Commerce. The
Department meeting held on 14th August,
2019 assigned Dr. K Pradeep Kumar,
Associate Professor of Commerce and Dr.Lt.
Sunilraj N.V., Assistant Professor of
Commerce as Co-ordinator and Cocoordinator respectively. An Organising
Committee consisting of Principal, Head of the
Department and other Faculty members of
Commerce was also constituted for preparing
th
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
programme schedule and necessary stages for
the successful conduct of the programme.
SELECTION OF PARTICIPANTS
As the theme is focused on Teachers and
Research Scholars, the organizing committee
decided to invite all teachers and research
scholars in various universities by clearly
explaining the theme and various modules
through the publicity materials. Personal emails were sent to Heads of the Department of
Commerce of various Colleges and
Universities to depute the teacher who is
handling the subject or in need of training in
the subject of Financial Econometrics. In
addition, brochures were sent to all Colleges,
Univerisity Departments and Self Financing
Colleges by clearly specifying the sequence of
modules included in different technical
sessions. Participants are requested to register
through e-mail by emphasizing their need for
participation in the programme. Based on the
response of the participants, 21 teachers from
various Colleges of Kerala and 24 research
scholars were screened and selected for
participation in the programme. Selection
memo is issued to all participants with strict
stipulation on adherence to attendance in all
days. In addition to that 9 faculty members of
Commerce Department, Govt. College,
Attingal and 5 full-time research scholars of
the Research Centre and 30 M.Com students
of the Department participated in the
Programme. Thus a total of 89 participants
attended the Five day workshop.
INAUGURATION
PRESENTATION
AND
THEME
In the inaugural session, Prof. Sunil S., Head
of the Department of Commerce welcomed the
participants. The Five day Workshop is
inaugurated by Dr. V. Manikantan Nair,
Principal Government College, Attingal. In his
inaugural address he appreciates the efforts
taken by the Research and P.G Department of
Commerce in promoting research in
Commerce and Management subjects through
thes series workshops organized successfully
every year. Dr. K. Pradeep Kumar, the Coordinator of the Workshop presented the theme
of the Workshop by quoting the series of
Workshops conducted successfully by the
Research and Post Graduate Department of
Commerce since 2015 in every October. He
mentioned the importance of this short term
training programme in imparting skills to
Teachers for handling the new subject and
molding their research capabilities in
econometric data analysis. Dr. S. Kevin, the
Chief trainer assigned for the first two days, in
his special address congratulates the
Departmental initiates by the faculty for
promoting research in the subject. Prof.
Lakshmi Chandrasekhar, the Vice Principal of
the College signified the importance of these
workshop and express her gratitude to the
Director of Collegiate Education for
sponsoring these valid seminars and
workshops. Dr. Lt. Sunilraj N.V, the Cocoordinator of the Workshop proposed vote of
thanks in the inaugural session. The inaugural
session concluded by 9.50 a.m.
TECHNICAL SESSIONS BY
DR. S. KEVIN
Technical sessions from I to VIII were
assigned to Dr. S. Kevin, the former Pro-Vice
Chancellor of University of Kerla and a former
Professor of Commerce. The first technical
session begins at 10.00 a.m with a brief
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
introduction of Chief Trainer by Dr. Anitha S.,
Assistant Professor of Commerce, Government
College, Attingal. In the first technical session
on Introduction to Financial Econometrics, Dr.
S. Kevin, clearly presented the importance of
Financial Econometrics and its applications in
Micro Economics including business and
industry. He answered the simple to complex
queries made by the participants patiently and
encourage them to widen the subject base by
reading books in Basic Econometrics. Tea and
snacks were served to participants as
refreshments during the sessions without any
tea break. Clean and safe drinking water is also
provided in the corner of the workshop venue.
In the second technical session, the trainer
focused on the importance of Normality of
Distributions in Econometric modeling. The
training was done through examples with
sufficient proofs to clarify the doubts of
participants. The second technical session
concluded by 1.00 p.m. Lunch break was
given for 30 minutes . In the third technical
session, the Chief trainer Dr. S. Kevin explains
the process of simple and multiple linear
regression analysis with the help of practical
examples. In the last technical session of the
first day, serial correlation and the use of
Durbin Watson Statistics was explained with
case studies. The first day of the workshop
concluded at 4.30 p.m. On 16th October,2019,
the technical session starts sharp at 9.30 a.m.
In the morning sessions, Stationarity of Time
series data and Unit root test and the problem
of heteroskedasticity were explained. A
complete learning atmosphere was clearly
visible in the whole sessions. Tea and snacks
were served during the sessions. The after
noon sessions after lunch started at 1.30 p.m .
In the beginning session, the problem of Multi
Collinearity was disussed with solutions and at
the concluding session, the concpt of Cointegration and its intricacies were discussed.
The second day comes to an end at 4.30 p.m.
Participants were given feedback form to
evaluate the technical sessions of Dr. S. Kevin
and the hospitality of the department in serving
the guest participants.
TECHNICAL SESSIONS BY
Dr.VIJAYAMOHANAN PILLAI N
Dr. Vijayamohanan Pillai, Professor of
Economics, Centre for Development Studies
has assigned 10 technical sessions in the
workshop to impart practical skills in
developing econometric models through sound
theoretical base and practical applications. He
explained the concepts in all technical sessions
through GRETL applications . The major
themes of various technical sessions are
advanced applications of Econometrics like
Time Series as a Stochastic Process, AR, MA
and ARMA processors, Tests for nonstationarity, ARIMA modeling, Co-integration
methods, Volatility models, Nature of Panel
data models, Panel data Error component
models, and random effects model. In addition
to these, he engaged a full session for training
participants in GRETL applications. After his
session participants are given hands on
training in the computer lab with example files
supplied by the Chief Trainer.In the practical
sessions, Dr. Binu R., Assistant Professor of
Commerce assisted the Chief trainer in
delivering the practical knowledge. In the
whole three days assigned to Dr.
Vijayamohanan Pillai. N., the participants are
anxiously hearing and observing the new sets
of knowledge and appreciated his skills in
training. In the whole three days also the
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
participants are provided with refreshments
and lunch on time. The organsing committee
collected the Feed Back form given for
evaluating the three day sessions assigned to
Dr. Vijayamohanan Pillai. N.
VALEDICTORY SESSION
The whole technical sessions completed by
3.00 p.m on 19th October, 2019. The
valedictory session started after tea break. The
session was chaired by Prof. S. Sunil, Head of
the Department of Commerce. Prof.
SibuKumar D, the incharge Principal delivered
the valedictory address after the presidential
address. In the presidential address, the head of
the department express warm regards to all
participants and suggest them to use the
knowledge gained from the workshop for their
academic and future research purposes. The
Report of the Workshop is presented by Dr.
Binu R, Assistant Professor of Commerce with
a briefing on all technical sessions by the two
resource persons. Certificates were distributed
to all participants by the Principal and Dr.
Vijayamohanan Pillai. N. Dr. K. Pradeep
Kumar, the Co-ordinator of the workshop
proposed Vote of Thanks to participants and
all stakeholders for its successful conduct. The
Valedictory session concluded at 4.00 p.m
with chanting of National Anthem. All
participants relived from the institution at 4.30
p.m.
Dr. K. PRADEEP KUMAR
Dr. Lt. SUNILRAJ N.V
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
TECHNICAL SESSIONS I TO VIII
Dr. S. KEVIN
Former Professor and Pro-Vice Chancellor, University of Kerala
objective to provide numerical values to the
Technical Session I
parameters
Origin of Econometrics
of
economic
relationships.
Econometrics applies statistical methods and
Jan Tinbergen is considered by many to be
one of the founding fathers of econometrics.
He was a famous Dutch Economist. He won
the first Nobel Prize in Economics in 1969
along with Ragnar Frish for developing
applied dynamic models in analysis of
economic processes. Ragnar Anton Kittil
Frisch a Norwegian Economist is credited
with coining the term in the sense in which it
is used today.
Econometrics means “economic measurement”
Econometrics may be defined as the social
science in which the tools of economic theory,
mathematics, and statistical inference are
to
phenomena.
the
analysis
[Arthur
of
S.
economic
Goldberger,
Econometric Theory, John Wiley & Sons,
New York, 1964, p. 1.]
model is a simplified representation of a real
world process. All the variables which the
experimenter thinks are relevant to explain the
phenomenon are included in the model. It is
"the
quantitative
analysis
of
actual
economic phenomena”
Econometrics and Statistics
relationships
In ordinary statistics, the empirical data is
collected, recorded, tabulated and used in
describing the pattern in their development
over time. Ordinary statistics is a descriptive
aspect of economics. It does not provide either
the explanations or measurement of the
parameters of the relationships. Econometrics
is typically used to explain how the economy
works. It deals with the measurement of
Econometrics deals with the measurement of
economic
explain phenomena and create models. A
Econometrics differs from ordinary statistics.
What is Econometrics?
applied
mathematical techniques to economic data to
(e.g.,
price
and
demand) . It is an integration of economics,
mathematical economics and statistics with an
economic relationships.
Basic Tool in Econometrics
A basic tool for econometrics is the multiple
linear
regression
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model.
In
modern
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
econometrics,
other
statistical
tools
are
3. Specification of the Econometric Model of
frequently used, but linear regression is still
Consumption
the most frequently used starting point for an
But relationships between economic variables
analysis.
are generally inexact. For example, size of
Methodology of Econometrics
family, ages of the members in the family,
1. Statement of theory or hypothesis.
family religion, etc., are likely to exert some
2. Specification of the mathematical model of
influence on consumption
the theory
To allow for the inexact relationships between
3.Specification
of
the
statistical,
or
economic variables, the econometrician would
econometric, model
modify the deterministic consumption function
4. Obtaining the data
as follows:
5.Estimation
of
the
parameters
of
the
Y = β1 + β2X + u
econometric model
where u is known as the disturbance, or
6. Hypothesis testing
error term.
7. Forecasting or prediction
It may well represent all those factors that
8. Using the model for control or policy
affect consumption but are not taken into
purposes
account explicitly.
1. Statement of Theory or Hypothesis
4. Obtaining Data
Keynes postulated that the marginal propensity
The Y variable in this example is the aggregate
to consume (MPC), the rate of change of
(for the economy as a whole) personal
consumption for a unit (say, a dollar) change
consumption expenditure (PCE) and the X
in income, is greater than zero but less than 1.
variable is gross domestic product (GDP), a
2. Specification of the Mathematical Model
measure of aggregate income, for 1982 – 1996
of Consumption
period (annual data).
Y = β1 + β2X
0 < β2 < 1
5. Estimation of the Econometric Model
where Y = consumption expenditure and X =
The regression analysis is the main tool used
income, and where β1 and β2, known as the
to obtain the estimates.
parameters of the model, are, respectively, the
Thus, the estimated consumption function is:
intercept and slope coefficients.
Y = −184.08 + 0.7064 X
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
6. Hypothesis Testing
misspecification. The test has proven to be
In our example we found the MPC to be about
0.70. Is this value statistically significant. This
produce the best model that will minimize the
7. Forecasting or Prediction
We may use the model to predict the future
value(s) of the dependent variable Y on the
basis of known or expected future value(s) of
the explanatory variable X. But forecast
errors are inevitable given the statistical
nature of our analysis. Residuals (difference
between the predicted values and the actual
Model misspecification occurs when some
important variables are omitted.The model
then will not account for some important
linearities.
cause
bias
Model
in the
remaining parameter estimates. An important
source of bias in OLS estimation is omitted
variables that are correlated with the included
explanatory variables.Often the reason for
omission
is
that
these
variables
are
unobservable (e.g., human ability). In such
cases, data on proxy variables can be used.
B.
Ramsey
1. Time series data
Time series data give information about the
numerical values of variables from period to
period and are collected over time
2. Cross section data
consumers or producers) at a given point of
time.
3. Panel data
The panel data are the data from repeated
survey of a single (cross-section) sample in
different periods of time.
4. Dummy variable data
They reflect only the presence/absence of a
characteristic. For example, the variable
`gender‟ takes two values – male or female.
These values can be represented as „1‟ for
male and „0‟ for female.
Features of Economic Data
Ramsey RESET Test
James
Structure of Econometric Data
variables concerning individual agents (e.g.,
Model Misspecification
misspecification will
forecast errors.
The cross section data give information on the
values) represent the forecast errors.
or
The Purpose of Econometrics
The purpose of Econometric analysis is to
can be tested using inferential statistics.
relationships
useful in detecting model misspecification.
(1969)
proposed
a
misspecification test known as Regression
Specification Error Test (RESET). The test
helps to find whether the model suffers from
Normality of data
Serial correlation or auto correlation
Stationarity of data
Cointegration of two data series
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Causality of variables
Example:
Multicollinearity
Advertisement is independent variable.
Heteroskedasticity
Sales
is
dependent
variable
Regression Equation- Example
Econometrics includes a study of these
features of economic data
Y – Sales
TECHNICAL SESSION II & III
X
–
Advertisement
(15,20,25,30,35)
Regression Analysis
Y=a+bX
Association between Variables
Y = 1500 + 100 X
The statistical tools used to study association/
This is an exact or deterministic relationship.
(3000,3500, 4000,4500,5000)
expenditure
relationships between variables are Correlation
and
Regression.
Correlation
Studies
the
association between variables that may be
related.
A measure of covariation. Indicates
magnitude and direction of covariation. Karl
Perason‟s Coefficient of Correlation (r) is the
widely used measure of correlation. r may be
positive or negative.r varies from (-)1 to 1.
Thus Regression analysis is a set of statistical
methods used for the estimation of relationships
between a dependent
variable and one or
more independent variables. Can be utilized to
assess the strength of the relationship between
variables
and
for
modeling
the
future
relationship between them.
Coefficient of determination is r square (R2).
Regression
Measures the extent of variation explained by
variations, such as linear, multiple linear, and
the relation. In research, universe parameters are
nonlinear. The most common models are
inferred from sample statistics. When correlation
simple linear and multiple linear.
analysis
includes
several
is significant, the inference is that correlation
exists in the population also. p-value indicates
the probability of the correlation occurring by
chance. If p-value is less than 0.05, correlation
is stated to be significant.
variables.
Related
Multiple independent variables are used in the
model. The mathematical representation of
multiple linear regression is:
Y = a + b1X1 + b2X2 + b3X3 + ϵ
Regression analysis is the Study of relationship
between
Multiple Linear Regression
variables
are
categorised as: Dependent and independent.
Where:Y – dependent variable, X1, X2, X3 –
independent
(explanatory)
variables
a
–
intercept (constant) b1, b2, b3 – slopes
(regression coefficients) ϵ – residual (error)
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Output of Regression Analysis
OLS Technique
The output consists of four important pieces of
Mathematically, regression uses a linear
information:
function
2
to
approximate
(predict)
the
(a) the R value represents the proportion of
dependent variable. Error is an inevitable part
variance in the dependent variable that can be
of the prediction-making process. Regression
explained
variable
uses a technique known as Ordinary Least
However, R2 is based on the sample and is a
Square (OLS) to reduce error to the lowest
positively biased estimate of the proportion of
level. OLS technique tries to reduce the sum of
the variance of the dependent
squared errors by finding the best possible
by
our
independent
variable
accounted for by the regression model (i.e., it
value of regression coefficients (β0, β1, etc.)
is too large)
(b) an adjusted R2 value which corrects
Regression
positive bias to provide a value that would be
Assumptions
expected in the population
There exists a linear and additive relationship
(c) the F value and its p-value indicating the
between dependent (DV) and independent
statistical significance of the regression model.
variables (IV). By linear, it means that the
F value is the ratio of explained variance to
change in DV by 1 unit change in IV is
unexplained variance of the model
constant. By additive, it means the effect of X
(d) the coefficients for the constant and
on Y is independent of other variables.
independent variable (with their t-values and
There
p-values) which is the information you need to
independent variables. Presence of correlation
predict the dependent variable, using the
in
independent variable
to Multicollinearity. If variables are correlated,
The Standard Error
it becomes extremely difficult for the model to
Is a measure of the precision of the model. It
determine the true effect of IVs on DV.
reflects the average error of the regression
The sumof the residuals (error) is zero.
model. We want the standard error to be as
The error terms must
small as possible.The standard error is used to
variance. Absence of constant variance leads
get a confidence interval for your predicted
to heteroskedestacity.
must
Analysis-
be
no
independent
values.
Government College, Attingal
Linear
correlation
variables
Model
among
lead
possess constant
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
The error terms must be uncorrelated. Presence
of
correlation
in
error
terms is
known
as Autocorrelation.
Homoskeasticity Vs. Heteroskedasticity
Homoscedasticity describes a situation in
which the error term is the same across all
The dependent variable and the error terms
must possess a normal distribution.
values
of
the
Heteroscedasticity
TECHNICAL SESSION IV
independent
(the
variables.
violation
of
homoscedasticity) is present when the size of
Hetereoskedasticity
Hetero (different or unequal) is the opposite of
Homo (same or equal)…Skedastic means
spread or scatter…Homoskedasticity = equal
spread Heteroskedasticity = unequal spread.
the error term differs across values of an
independent variable. The impact of violating
the assumption of homoscedasticity is a matter
of degree, increasing as heteroscedasticity
increases.
Refers to non constant volatility.A sequence of
random variables is heteroskedastic, if the
random variables have different variances
signifying high and low volatility.A sequence
of random variables is called homoskedastic if
it has constant variance.
Mandelbrot
Unconditional: is predictable, and most often
relates to variables that are cyclical by nature.
can include higher retail sales reported during
the traditional holiday shopping period or the
increase in usage of electricity during warmer
Volatility Clustering
Benoit
The Types of Heteroskedasticity
defined
it
as
the
observation that "large changes tend to be
months. future periods of high and low
volatility can be identified.
followed by large changes, of either sign, and
Conditional: is not predictable by nature.
small changes tend to be followed by small
There is no sign that leads analysts to believe
changes”.High volatility and low volatility
data will become more or less scattered at any
occur in alternating clusters. This volatility
point
clustering is known as heteroskedasticity.
considered
When heteroskedasticity is autocorrelated, it is
heteroskedasticity as not all changes can be
known
attributed to specific events or seasonal
as
auto
regressive
heteroskedasticity (ARCH ).
conditional
in
time.
Financial
subject
to
products
are
conditional
changes. future periods of high and low
volatility cannot be identified
Examples: share prices, exchange rates
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Consequences of Heteroskedasticity
 Glejser test
Homoskedasticity is needed to justify the usual
 Brown–Forsythe test
t tests, F tests, and confidence intervals for
 Harrison–McCabe test
OLS (Ordinary Least Squares) estimation of
 Breusch–Pagan test
the linear regression model, even with large
 White test
sample sizes. In case of heteroskedasticity, the
 Cook–Weisberg test
OLS estimators are no longer the BLUE (Best
Models of Heteroskedasticity
Linear Unbiased Estimators) because they are
ARCH –type models are commonly employed
no
in modeling financial time series that exhibit
longer
efficient,
so
the
regression
predictions will be inefficient too.
time-varying volatility and volatility clustering
Importance of Heteroskedasticity
(heteroskedasticity). Commonly used ARCH
Is important in interpreting linear regression
models
Is the extent to which the variance of the
conditional heteroskedasticity)
residuals depends on the predictor variable.
GARCH
The residual in linear regression is the amount
conditional heteroskedasticity) model
are
ARCH
(Auto
(Generalized
auto
regressive
model and
regressive
of difference between the actual outcome and
the
outcome
predicted
by
Heteroscedasticity (the
the
violation
model.
of
TECHNICAL SESSION V
Multicollinearity
homoscedasticity) is present when the size of
The Independent variables in a multiple
the error term (residual) differs across values
regression model should be independent. Multi
of an independent variable. The residuals
collinearity
represent the error of your model. If the
variables in a regression model are correlated
amount of error in your model changes as the
It can cause problems when you fit the model
variables change, then you do not have a very
and interpret the results.
good model.
Why is Multicollinearity a problem?
Tests for Heteroskedasticity
A key goal of regression analysis is to estimate
There are several methods to test for the
the relationship between each independent
presence of heteroscedasticity
variable and
occurs
when independent
the dependent
variable.
The
 Levene's test
regression
 Goldfeld–Quandt test
change in the dependent variable for 1 unit
 Park test
change in an independent variable when
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coefficient represents
the mean
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
you hold all of the other independent variables
between
constant.It becomes difficult for the model
strength of that correlation. Statistical software
to estimate the
calculates a VIF for each independent variable
independent
relationship
variable
between
and
variable independently
the
each
dependent
variables
and
the
Calculation of Tolerence and VIF
the
The VIF may be calculated for each predictor
independent variables tend to change in unison
by doing a linear regression of that predictor
when there is multicollinearity.
on all the other predictors, and then obtaining
Types of Multicollinearity
the R2 from that regression. Tolerance is (1-
Data multi collinearity: present in the data set
R2). The VIF is 1/(1-R2). It is called the
itself.Structural multi collinearity: when a
variance inflation factor because it estimates
new independent variable is created from the
how much the variance of a coefficient is
data set (for example, a variable value is
“inflated” because of linear dependence with
squared to create another variable)
other predictors. Thus, a VIF of 1.8 tells us
When
there
is
because
independent
no
need
to
reduce
error) of a particular coefficient is 80% larger
Multicollinearity?
If you have only moderate multi collinearity.
If multi collinearity is not present for the
independent variables that you are particularly
If your primary goal is to make predictions,
and you don‟t need to understand the role of
completely uncorrelated with all the other
predictors.
Tolerance and the Variance Inflation Factor
(VIF) are two collinearity diagnostic factors
that can help to identify multi collinearity. The
variable‟s tolerance is 1-R2. A small tolerance
value indicates multicollinearity. The Variance
Inflation Factor (VIF) is the reciprocal of
inflation factor (VIF)
value of 1 indicates that there is no correlation
others. VIFs between 1 and 5 suggest that
Testing for Multicollinearity
1/Tolerance.
VIFs start at 1 and have no upper limit. A
between this independent variable and any
each independent variable.
or
than it would be if that predictor was
Variance Inflation Factor (VIF)
interested in.
Tolerance
that the variance (the square of the standard
The
identifies
variance
there is a moderate correlation, but it is not
severe enough to warrant corrective measures.
VIFs greater than 5 represent critical levels of
multi collinearity where the coefficients are
poorly estimated,
and the p-values are
questionable.If the value of tolerance is less
than 0.1 and, simultaneously, the value of VIF
correlation
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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
10 and above, then the multi collinearity is
2. Skewness method
problematic.
3. Tests of Normality
How to resolve structural multicollinearity?
Histogram method is a graphical method
Structural multi collinearity can be resolved by
Skewness method and Tests of Normality
centering the independent variables. Centering
make use of descriptive statistics.
the variables is also known as standardizing
Histogram Method
the variables by subtracting the mean. This
Looks at a histogram of the data with the
process involves calculating the mean for each
normal curve superimposed.
continuous independent variable and then
Normal Data
subtracting the mean from all observed values
of that variable. Then, use these centered
variables in the model.
How to resolve Data Multicollinearity?
The potential solutions include the following:
Remove some of the
highly correlated
independent variables. Linearly combine the
independent variables, such as adding them
Non-normal Data
together. Perform an analysis designed for
highly correlated variables, such as principal
components analysis or partial least squares
regression. If you can accept less precise
coefficients, or a regression model with a
high R-squared but hardly any statistically
significant variables, then not doing anything
Evaluation of Histogram Method
about the multicollinearity might be the best
This method provides a sense of the normality
solution.
of data. All samples deviate somewhat from
TECHNICAL SESSION VI
normal, so the question is how much deviation
Normality of Data
from the black line indicates “non-normality”
There are three interrelated approaches to
Histogram provides no hard-and-fast rules.
determine normality of data
1. Histogram method
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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Descriptive Statistics
The value of skewness is 1.797. The question
Quantities computed from the data set to
is “how much” skewness render the data non-
describe different characteristics of the data set
normal. This is an arbitrary determination, and
(central
sometimes difficult to interpret using the
value,
peakedness).
variability,
symmetry,
Mean, median, mode, Measures
of variability, Minimum, maximum, range,
Quartiles,
interquartile
range,
percentiles
values of Skewness.
Tests for Normality
Standard deviation, variance(Mean +/- 2 SD =
Tests for normality take into account both
95 percent of observations) Skewness :zero
Skewness
value represents symmetry; positive value
(peakedness)
implies right skewed distribution; negative
Kolmogorov-Smirnov (K-S) test and Shapiro-
value
Wilk (S-W) test are designed to test normality
implies
left
skewed
distribution.
(symmetry)
and
Kurtosis
simultaneously.
Measures of peakedness/flatness :Kurtosis:
by comparing
absolute kurtosis for normal distribution is 3;
distribution with the same mean and standard
relative kurtosis = absolute value – 3Positive
deviation of your sample. Jarque–Bera test is
value indicates peaked curve.Negative value
another test used for testing normality of data
indicates flat curve.
your
data to
The
a
normal
Interpretation of test results
Skewness measures the symmetry of the
distribution. Skewness is 0 in a normal
distribution; so the farther away from 0, the
more non-normal the distribution. A positively
If the test result is significant (p-value less
than .05), then the data are non-normal. If the
test is NOT significant (p-value greater than
0.05), then the data are normal.
skewed distribution has scores clustered to the
left, with the tail extending to the right. A
Example
negatively skewed distribution has scores
clustered to the right, with the tail extending to
the left
Evaluation of Skewness Method
The histogram above for variable2 represents
positive skewness (tail extending to the right)
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Jarque Bera (JB) Test
JB = [5] X [1.5625 + 0.0625]
The Jarque–Bera test is a goodness-of-fit
test of whether sample data have the skewness
and kurtosis matching a normal distribution.
The test is named after Carlos Jarque and
Anil K. Bera.
JB = 8.125
TECHNICAL SESSIONS VII &VIII
ANALYSIS OF FINANCIAL TIME SERIES
Outline
Durbin-Watson
test
for
testing
serial
The test statistic JB is defined as:
correlation or autocorrelation
JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2]
Unit root test for testing stationarity of time
where
series data
n is the number of observations,
Cointegration of nonstationary variables
S is the sample skewness,
What is a Time Series?
C is the sample kurtosis,
k is the number of regressors (being 1 outside
a regression context)
spaced time intervals, such as years, months,
For Normal Distribution
days, hours, etc.
Values of the Distribution
n = 30 S = 0 C = 3
A series of data relating to different equally
Examples:
k=1
The closing prices of the share of ICICI Bank
Formula for test statistic
JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2]
Computation of test statistic
JB = [(30 – 1 + 1)/6] X [02 + ¼(3 – 3)2]
for 320 consecutive days. US Dollar exchange
rates recorded for 280 consecutive trading days
Significance of Time Series Analysis
JB = [5] X [0]
Time series analysis attempts to understand the
JB = 0
past
For Non-normal Distribution
procedures and techniques, including statistical
tools and econometric models, are used for the
Values of the Distribution
n = 30 S = 1.5 C = 4
and predict the future. Systematic
purpose.The primary objective is to detect
k=1
regularities and structures in data that will be
Formula for test statistic
JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2]
Computation of test statistic
JB = [(30 – 1 + 1)/6] X [1.52 + ¼(4 – 3)2]
helpful in forecasting future values of the
variable.The forecasted future values are
useful in planning and policy making activities
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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
For example, the forecasted exchange rates of
and the lagged time series indicates the
foreign currencies can help the exporters and
presence of autocorrelation in the original time
importers in planning and managing their
series. Autocorrelation may be defined as the
foreign exchange risk.
correlation between observations of a time
A Financial or Economic Time Series
series
Contains
data
regarding
financial
and
at
different
distances
apart.
Autocorrelation confirms non-randomness in
economic variables such as interest rates, share
the data series.
prices, exchange rates, etc. Data in a financial
Significance of Serial Correlation
or economic time series are expected to be
The essence of serial correlation is to see how
random variables as fluctuations in these data
sequential observations in a time series affect
are uncertain and unpredictable. The daily US
each other. If we can find the structure in these
Dollar exchange rates are considered as
observations it will help us improve our
random variables and the series may be
forecasts and simulation accuracy.
considered as a stochastic time series (as
opposed to a deterministic time series).
Durbin-Watson Test
Are the data in a financial time series
Durbin-Watson test is popularly used to detect
random variables?
the presence of autocorrelation in time series
Random Variables
data. A test that the residuals from a linear
Random variables are independent of each
regression
other. If the data are related to each other, they
independent. Original time series can be taken
represent non-random variables.
as the independent series (Xt).Lagged time
Randomness of Data in a Financial Time
series can be taken as the dependent series (Yt)
Series
An
important
or
multiple
regression
are
Regression equation: Yt = α + β Xt
feature
to
be
examined.
serial
The residuals from the linear regression
correlation or serial dependence, is an
between the original series and the lagged
important feature of time series data. If a new
series can be used for the test
Autocorrelation,
also
known
as
series is created by taking the daily exchange
rates with time lag of one day and is compared
with the original time series, existence of a
covariation between the original time series
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Original
and
lagged
Share prices of Asian Paints
Days
time
series
Hypothesis for Testing
Spot price
Spot price
(Original)
(Lagged)
1
1279.8
1251.75
2
1251.75
1224.7
3
1224.7
1209.5
4
1209.5
1247.8
5
1247.8
1201.4
6
1201.4
1225.95
7
1225.95
1219.05
8
1219.05
1261.2
ei
(ei - ei-1)2
ei2
9
1261.2
1250.3
4.97
-
24.67
10
1250.3
1267.6
-15.46
417.39
239.1
11
1267.6
1259
-24.28
77.73
589.48
12
1259
1235.4
17.61
1754.54
310.04
13
1235.4
-37.83
3073.46
1431.17
-2.33
1260.28
5.43
-15.02
161.13
225.73
944.75 1916.55
+ 0.236 Xt + et
826.8
7.91
434.61
62.52
27.78
394.91
771.68
15.1
160.85
227.9
-6.47
465.28
41.91
10116.74
4756.44
Residual Calculation
Regression equation:
Yt = α + β Xt + et
Yt = 944.75 + 0.236 Xt + et
Yt =
28.75
This equation can be used to calculate the
predicted values of the variable. The difference
between the observed values and the predicted
values are the residuals
Days
Total
Spot price
Predicted
Residuals
(Lagged)
prices
1
1251.75
1246.78
4.97
2
1224.7
1240.16
-15.46
3
1209.5
1233.78
-24.28
4
1247.8
1230.19
17.61
and dL have been tabulated for different values
5
1201.4
1239.23
-37.83
of k (the number of explanatory variables) and
6
1225.95
1228.28
-2.33
7
1219.05
1234.07
-15.02
8
1261.2
1232.45
28.75
9
1250.3
1242.39
7.91
10
1267.6
1239.82
27.78
11
1259
1243.9
15.1
12
1235.4
1241.87
-6.47
Testing of Hypothesis
d becomes smaller as the serial correlation
increases. Upper and lower critical values, dU
n (sample size)
If d < dL reject H0 : ρ = 0
If d > dU do not reject H0 : ρ = 0
If dL < d < dU test is inconclusive
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Durbin Watson Test Statistic
Stationarity
Stationarity and
autocorrelation are
two
important features of time series data. A time
series is an example of a stochastic process,
which is a sequence of random variables
ordered in time. A time series may be
stationary or non stationary. It is said to be
stationary if its mean and variance are constant
Critical Values of Durbin Watson Statistic
Sample
size
Significance
level
over time or are independent of time.
k=1
k=1
k= 2
k= 2
DL
DU
DL
DU
A stationary
time
series
is
one
whose
statistical properties (such as mean and
variance) are constant over time.
30
0.05
1.35
1.49
1.28
1.57
40
0.05
1.44
1.54
1.39
1.6
50
0.05
1.5
1.59
1.46
1.63
60
0.05
1.55
1.62
1.51
1.65
80
0.05
1.61
1.66
1.59
1.69
of different time periods within the overall
100
0.05
1.65
1.69
1.63
1.72
time period of the time series. Mean and
150
0.05
1.72
1.75
1.71
1.76
200
0.05
1.76
1.78
1.75
1.79
Non-stationary Time Series
A time series may be subdivided into subsets
variance for the different subsets may be
calculated. The mean may vary for the
different subsets depending on whether the
An Example
values are increasing or decreasing over time.
Time series data: Share prices of Asian Paints
The variance may also vary for the different
for 497 days
sub periods. A time series whose mean and
Durbin-Watson test result:
variance vary across different time periods is
d = 1.9844
said to be non-stationary
dL (for α = 0.05 and k =1) = 1.76 (n = 200)
Asian Paints Share Prices-Mean and Variance
dU (for α = 0.05 and k =1) = 1.78 (n = 200)
Since d > dU, H0 cannot be rejected; it is
accepted.
Conclusion: There is no autocorrelation in the
share price series.
Period
21st March 2017 - 11th
Aug. 2017
14th Aug 2017 - 5th Jan.
2018
8th Jan. 2018 - 4th June
2018
5th June 2018 - 30th Oct.
2018
31th Oct 2018 - 20th
March 2018
Entire period
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Observations
Mean
Variance
100
1122.73
1270
100
1168.02
1203.23
100
1176.35
4026.11
100
1319.69
5666.42
97
1372.28
3210.71
497
1230.97
12310.3
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Importance of Stationarity
Why should we worry whether a time series is
stationary or not? If it is nonstationary, it is not
possible to generalize it to other time periods.
If we have two independent non-stationary
series, then we may find evidence of a
relationship when none exits (i.e. spurious
regression problem). The relationship will be
genuine only if the two series are cointegrated.
Correlogram
Autocorrelation is the correlation between
observations of the original time series and the
Economic and Financial Time Series
Examples: exchange rates, share prices, These
are often trending and consequently nonstationary. It is important to test whether the
economic or financial time series is non-
lagged time series. The lagged time series may
be created with lag of one time period or more
than one time period. ACF (Autocorrelation
Function) gives the correlation coefficients
calculated for several lagged time series with
increasing
stationary.
lag
periods.
A plot
of the
correlation coefficients against the lag length is
Tests of Stationairity
known as correlogram.
1. Graphical analysis
ACF of Asian Paints Share Prices
2. Correlogram
3. Unit root test
Graphical Analysis
Plot the time series in an XY graph. Gives an
initial clue as to whether the time series is
stationary or not. Starting point for more
formal tests of stationarity.
Graph of Share Prices of Asian Paints
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Correlogram of Asian Paints Share Prices
Null hypothesis:
There is unit root and time series in nonstationary
Alternative hypothesis:
There is no unit root and time series is
stationary
The null hypothesis is rejected if the test
statistic is more negative than the critical value
Critical Values for DF and ADF tests
ACF Interpretation
The table shows whether the correlation
Significance level
CV for constant
but no trend
CV for constant and
trend
5 percent
(-) 2.86
(-) 3.41
1 percent
(-) 3.43
(-) 3.96
coefficients are significant. Q-stat and its pvalue
indicates
if
the
autocorrelation coefficients
sum
of
the
is statistically
significant. ACF has to be examined to see if
the correlation of the time series over several
ADF test Result for Asian Paints Share
lags decays quickly or slowly. If it decays
Prices
slowly, it is an indication that the time series is
non-stationary.
Unit Root Test
A statistical procedure used to test whether a
time series is non-stationary and possesses a
unit root. A series which has a unit root is
non-stationary.
Augmented
Dickey-Fuller
(ADF) test is commonly used to test the null
hypothesis that a time series has a unit root and
is non-stationary.
Dickey Fuller and Augmented Dickey Fuller
Tests
Dicky-Fuller and Augmented Dicky Fuller
Null Hypothesis: t
series has a unit root
Exogenous:
Constant
Lag Length: 1 (Automatic Based on AIC,
MAXLAG=10)
tests for unit root.
t-Statistic
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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Augmented
Dickey-Fuller test
statistic
Test
critical
1%
values: level
5%
level
10%
level
difference of difference) to make it stationary,
-1.175441
0.686438
it is integrated of order two, denoted as I(2).
-3.443379
If it has to be differenced d times to make it
-2.867168
stationary, it is said to be integrated of order d,
-2.569812
denoted as I(d). A stationary time series is
integrated of order zero, denoted as I(0).
Inference
Cointegration
The ADF test statistic (tau value) is (-)
1.17544. As this value is not more negative
than the critical values, the null hypothesis
cannot be rejected. Null hypothesis that the
time series has unit root and is nonstationary
is accepted
In the case of two independent non-stationary
series, we may find evidence of a relationship
when none exists (i.e. spurious regression
problem). The relationship will be genuine
only if the two series are cointegrated. It
becomes necessary to examine the existence of
Meaning of Unit Root Test
cointegration in such cases.
In an autoregressive (AR) statistical model of a
time series, the AR parameter is assumed to be
1. In a data series Yt modelled by
Yt+1 = aYt + et
Concept of Cointegration
An old woman and a boy are on random walk
in the park. Information about the boy‟s
location tells us nothing about the old
„a‟ is an unknown constant
woman‟s location. There is no cointegration
Unit root test would be a test of the hypothesis
that a = 1, against the alternative that „a‟ is
less than 1. If the time series has unit root (a =
1), the series is said to be nonstationary.
Integrated Time Series
An old man and his dog are joined by a leash.
The man and the dog are each on a random
walk. But they cannot wander too far from
each other because of the leash. The random
processes
A nonstationary time series is known as an
integrated time series or series with stochastic
trend. It could be made stationary by
differencing (that is, subtracting the preceding
value from it current value). If a time series
becomes stationary after differencing it once, it
is said to be integrated of order one, denoted as
describing
their
paths
are
series
are
whether
the
cointegrated.
Testing for Cointegration
To
test
whether
cointegrated,
we
two
I(1)
examine
residuals are stationary or I(0). In the case of
two nonstationary I(1) series, Y and X, if the
residuals of the regression Yt = α + β Xt + et are
I(1). If it has to be differenced twice (i.e.,
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
stationary, then the variables are said to be
cointegrated.
The Engle-Granger test is used for testing
cointegration between variables.
Example
Two series are considered for the analysis
Nifty values are taken as the independent
variable. Asian Paints share prices are taken as
the dependent variable
No. of observations: 497
Regression analysis is proposed for studying
the relationship between the variables.The two
series are financial time series and hence
stationarity of the series have to be examined.
If the series are nonstationary, then existence
of cointegration has to be examined before the
regression results can be relied upon.
Results of Engle-Granger Cointegration
Test
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
No. of observations: 76
Regression analysis is proposed for studying
the relationship between spot price and futures
price. The two series are financial time series
and hence stationarity of the series have to be
examined. If the series are nonstationary, then
existence of cointegration has to be examined
before the regression results can be relied
upon.
Results of Engle-Granger Cointegration
Test
Inference
The two time series of Nifty values and Asian
Paints share prices are nonstationary. The
Residual series of the regression between the
two series is also non stationary. Cointegration
exists only if the residual series is stationary.
There is no cointegration between the two
series, as the residuals series is nonstationary.
The regression results are not reliable, in the
absence of cointegration.
Another Example
Two price series are considered for the
analysis. Spot prices of Asian Paints are taken
as the independent variable. Futures prices of
Asian Paints are taken as the dependent
variable
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Inference
The two time series of Spot prices and Futures
prices of Asian Paints are nonstationary. The
Residual series of the regression between the
two series is stationary. Cointegration exists if
the residual series is stationary. There is
cointegration between the two series, as the
residuals series is stationary. The regression
results are reliable, as the variables are
cointegrated.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
TIME SERIES REGRESSION
Dr. P.N. Harikumar
Associate Professor, Catholicate College, Pathanamthitta
INTRODUCTION
Time series is a set of observations generated
sequentially in time. If the set is continuous
then the time series is continuous. If the set is
discrete then the time series is discrete.
Generally discrete time series are more
adopted in Econometric studies. “Sequential
in time” is only to mean successive
observations and hence a sequence of
observations observed over a space may also
be considered as a time series. Generally the
term „Time Series‟ is used to refer this kind
of data also. Analysis of discrete time series
is relatively easier. Usually time series are
observed over equal interval of time.
However, this is not a restriction on the
scope of time series analysis.
The usage of time series models is twofold:
1. Obtain an understanding of the underlying
forces and structure that produced the
observed data
2. Fit a model and proceed to forecasting and
monitoring.
Examples of Time series data
Business and Economics : weekly share
prices, monthly profits, sales forecasting,
budgetary analysis, stock market analysis,
yield projections
Meteorology: daily rainfall, wind speed,
temperature
Sociology : crime figures, employment
figures
DESCRIPTIVE UNDERLYING FORCES
A time series is said to be a consequent effect
of four possible forces acting at a point of
time. They are trend, seasonal variation,
oscillation and random component. A Time
series is said to be an effect of these four
components and the researcher may choose
from the two alternative models ie. Additive
or multiplicative models. In an additive
model, these forces or components are added
up to give time series. This means that at
every point of time these four forces may be
in operation and hence there may be an effect
due to Trend (T), an effect due to Seasonal
Variation (S), Oscillation (O) and Random
component (R) and the value of observation
of the variable at that point of time is taken
as the sum of these four effects. It the
variable is taken as y and its observation at
time „t‟ is denoted by yt then it is assumed to
be given by yt = Tt+St+Ot+Rt. Similarly the
alternative model will be obtained by
multiplying these effects to get yt.
A study of time series aims at identifying the
possible contributions of these effects and
after eliminating these effects the remaining
series called as „Residual Series‟ is taken up
for high end solutions using different types
of models. However, the identification of
effects due to Trend and Seasonal Variations
are highly valuable in Econometric Studies.
Definitions
Trend: A smooth movement of observations
either upwards or down wards over a
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
relatively long period of time is called a
Trend.
Possible reasons for trend in
observed series may be attributed to
development of dependent factors like
technology, health and the like.
Seasonal Variation : In certain cases of time
series a systematic behavior of up and down
movement can be observed repeatedly over a
fixed period of time interval. As an example,
textile sales may be seen to vary in a
systematic manner over a year. This type of
series may provide valuable information that
may be useful in future predictions.
Oscillations: Suppose a time series is devoid
of effects due to trend and seasonal variation,
then it may be observed to oscillate around a
constant value. A look at recordings from
ECG or variation in the movement of sensex
index over a normal day may be examples.
Random Component: Above all the other
effects having a possibility of explanation,
there may be a large number of forces acting
and adding a random effect to the series.
A study of time series is directed at the
estimation of explainable effects due to
Trend and Seasonal Variation; eliminate
them from the observed series; try for
explanation of the residual series using
statistical models. In order to achieve them,
there are conventional methods having a well
built computational capability. Also, there
are high end methods involving modeling
under different assumptions.
Use of time series is an enchanting area in
Time Series Analysis. The types of analyses
vary with respect to the type of the series.
Empirical work assumes that the underlying
time series is stationary. The second concept
concerned with time series analysis is Auto
Correlation. Presence of auto correlation may
turn a time series into Non Stationary. Using
Regresson with two time series may produce
Spurious or nonsense regression. The Rsquare may be very high, yet the regression
will be meaningless.
The key concepts to be considered will be the
concepts called Stochastic Processes, which
are taken as concepts generating the time
series. Stationary Processes, Pure Random
Processes,
Non-stationary
Processes,
Integration Variables, Co-integrtion and Unit
Root Test are explained in sequence.
Random Process or Stochastic Process is a
collection of random variables, usually
ordered in time. Such processes can be
defined on other spaces also. They may be
further classified into discrete and continuous
depending upon the observations are in
continuum or at discrete points.
Stationary Stochastic Process: A stochastic
process is said to be stationary if its mean
and variance are constant over time and the
covariance between two time points depends
upon only the time difference and not on the
choice of the time points. A majority of time
series assumes that the underlying process is
stationary. If a time series is not stationary in
this sense, it is called as non-stationary. For
example Random Walk Model, used in the
study of stock prices comes under this head.
Random Walk Model is considered in two
ways; Random Walk Model with Drift and
Random Walk Model without Drift. Let Yt
be the variable observed at time point „t‟,
then the RWM is stated as
Yt= Yt-1 +ut
Where ut is an error term with mean 0 and
variance σ2.It can be observed that this
results in
Y1 = Y0+u1
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Y2 = Y1+u2 = Y0+u1+u2
Y3 = Y2+u3 = Y0+u1+u2+u3
As the time goes on increasing, the mean will
remain the same but the variance will go on
increasing making the series non-stationary.
Similarly, the RWM with drift can be written
as Yt=ᵭ +Yt-1+ut where ᵭ is the drift
parameter. The difference between
Yt – Yt-1 = ᵭ+ut. drifts upwards or downwards
depending upon the value ᵭ. Here, both mean
and variance go on increasing over time,
suggesting that the series is non-stationary.
Unit Root Stochastic Process: Suppose we
write the RWM as Yt=ρYt-1 +ut where
-1 ≤ ρ ≤ 1 is the classical autoregressive
model. When ρ is 1, the model is called Unit
Root Stochastic Process. The model leads to
a non stationary series observed earlier. Thus
non-stationarity, Random Walk and Unit
Root stochastic process are equivalent
concepts . If lρl ≤1, the model leads to
stationary series.
Integrated Stochastic Process: RWM is but
a specific case of a more general class of
models called as Integrated Model. It was
claimed in the last part that the RWM
without drift is non-stationary. But its first
difference (Yt-Yt-1) = ∆Yt=ut is stationary.
Hence, we call RWM without drift as „an
Integrated Process of Order 1” Similarly, if
the difference of the first difference is
stationary process is called as Integrated
process or order 2. In general if pth order
differences of a series results in a stationary
series, the process is called as „Integrated of
order p‟ denoted as I(ρ).
Co-integration: The regression of a nonstationary time series on another nonstationary time series may produce a spurious
regression. Let us suppose that we consider
the Y and X time series. Subjecting these
time series individually to unit root analysis
and suppose you find that they both are I(1);
that is they are auto-regressive of order 1
with autoregressive coefficient being 1; that
is, they contain a unit root. Suppose, then,
that we regress Y on X as follows.
Yt = β1+β2Xt+ut
(1)
Let us write this as
ut = Yt-βt-β2Xt
(2)
Suppose we now subject ut to unit root
analysis and find that it is stationary; that is.
It is I(0). This is an interesting situation, for
although Yt and Xt are individually I(1), that
is, they have stochastic trends, their linear
combination (2) is I(0). So to speak,the linear
combination cancels out the stochastic trends
in the two series. If you take consumption
and income as two I(1) variables, savings
defined as income – consumption could be
I(0). As a result, a regression of consumption
on income as in (1) would be meaningful. In
this case we say that the two variables are cointegrated.
A number of methods for testing cointegration have been proposed in the
literature. Two simple methods are the DF or
ADF unit root test on the residuals estimated
from the co,-integrating regression and the
Co-integrating regression Durbin-Watson
test. Engle and Granger have calculated the
critical significance values. Therefore, the
DF and ADF tests in the present context are
known as Engle-Granger (EG) and
Augmented Engle-Granger (AEG) tests
The Unit Root Test : A test of stationarity
that is popular is the Unit Root Test. For this
purpose Dickey and Fuller have developed a
test for δ =0. The associated statistics is
called as Tau-statistics using simulation
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
exercises and prepared extensive tables
providing critical values. This is called as DF
test for stationarity. This test assumes that the
error term ut is uncorrelated. But in the case
where ut are correlated, Dickey-Fuller
developed another test called Augmented
Dickey Fuller test (ADF test). This is done
with a modification of the considered model
by adding the lagged values of the dependent
variable. Still δ=0 is tested and the same
tables for DF test can be used here also.
Approaches to Time Series Forecasting
There are different approaches to forecasting
with time series such as Exponential
Smoothing, Single Equation Regression
Models, Simultaneous Equations Models,
ARIMA Models and VAR Models. The
ARIMA Models coming under Box-Jenkins
Methodology include Auto-Regressive(AR),
Moving Average (MA) and Auto Regressive
Integrated Moving Average (ARIMA)
Models. Here a brief view of these
techniques is given.
Auto Regressive (AR) Process: Let Yt be
the variable at time „t‟ in the process. Then in
some situation, this may linearly depend
upon its value in the preceding time point or
points. This can be written as Yt=α1Yt-1+ut,
which is called as a first order Auto
Regressive Process (AR(1)). The current
year GDP may depend upon last year GDP.
The model may be written in a modified way
as (Yt-δ)=α1(Yt-1-δ)+ut, where δ is the mean
of pth order Auto Regressive Process
(AR(p)).
Moving Average (MA) Process: In the same
way, consider that realization of a variable at
a time point „t‟ is given by a disturbance of a
value, say average of a stationary aprocess,
the model can be specified as Yt=µ+ut,
where ut is a random variable with mean 0
and variance σ2, called an error term (with
normal distribution for the error term, it is
called as white noise). Extending this idea,
that the error at time „t‟ is a weighted average
of previous and current errors, then the
model can be written as
Yt= µ+β0ut+β1ut-1, called the first order
Moving Average process. Extending this
ideas, to „q‟ previous time points we get a
model called as qth order Moving Average
Process MA(q).
Auto Regressive Moving Average (ARMA)
Process: If there is a reason to believe that a
process Yt has the characteristics of both AR
and MA process, then another model can be
obtained with ARMA models, given by
Yt=θ+αtYt-1+β0ut+β1ut-1
Notice the presence of lag of order one only
for AR and MA components. This is called
ARMA(1,1) Process. In general, one can
define ARMA (p,q) processes, where the AR
process is of order „p‟ while that of MA of
order „q‟.
Auto Regressive Integrated Moving
Average (ARIMA) Process: It is already
stated that the analysis is comfortable when
the series is a stationary series. If the given
series is stationary, it is called I(0),
„Integrated of order 0‟. Generally, the given
series is difference for required number of
successive repetitions to get into stationarity.
If a given series is differenced for „d‟ times
before applying ARMA (p,q) model, then the
resulting model is called as ARIMA(p,d,q)
Model.
In order to estimate the model and use the
results for forecasting, Box-Jenkins method
is used. While considering ARIMA (p,d,q0
model, knowledge about p,d,q are not
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
known. Hence, the most important issue is
that of deciding these constants. Box-Jenkins
methodology consist of four steps for
analyzing a series and uses the results for
forecasting with ARIMA(p,d,q) model. They
are identification, Estimation, Diagnostic
Checking and Forecasting. This is a high end
model activity which needs expert assistance.
ARCH and GARCH Models
Auto Regressive Conditional
Heteroscedasticity
(ARCH)
and
Generalised Auto Regressive Conditional
Heteroscedasticity (GARCH) models are
models useful in the study of time series
exhibiting Volatality Clustering. By volatility
clustering it meant that the behavior of the
series showing wide swings for a time
interval followed by periods in which there is
relative calm. This implies that the variance
of the series varies over time. This
heteroscedasticity or unequal variance may
have an autoregressive structure, ie variance
observed over different points of time may
be auto correlated. Generally Xt2 is taken as a
measure of volatility. Accepting this, one
way of verifying such volatility clustering
will be to model the behavior using AR(1)
model, Xt2= β0+β1X2t-1+ut in the simplest
case, where ut is the usual error term. If β1 is
0, there is no volatility; hence a test is
performed for the hypothesis β1=0 with the
usual test. Depending upon need higher
order models can be used. If the hypothesis
is rejected, the presence of volatility
clustering is accepted then the series presents
an ARCH situation. What should be done in
that case? In such situations, the regression is
obtained by the Generalised Least Square
Method. There are many software providing
comfortable computation here. It is to be
noted that a significant Durbin Watson
Statistic should be further followed to check
for ARCH effect also.
With this addition to regression models,
other improvements started to come in the
area that has resulted in a more general
format for ARCH models called as
Generalised Autoregressive Conditional
Heteroscedasticity (GARCH) model. We
consider simple GARCH(1,1) model; in fact
hese are certain principles which can be
adopted for any model. GARCH (1,1) can be
written as
σt2=α0+α1ut-12+α2σt-12
This implies that the conditional variance of
„u‟ at time‟t‟ in the model, where „u‟ is the
error term, depends upon not only on the
squred error term in the previous time‟t-1‟
(as in ARCH) but also on its conditional
variance of the previous time period. This
model can be genaralised into GARCH(p,q)
where p lagged terms of the squared error
and q lagged terms of the conditional
variances. Again, estimation of such models
are by GLS method with computation of
required matrices from the data using
software.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
INTRODUCTION TO GRETL
Dr. K. PRADEEP KUMAR
Associate Professor
Government College
Attingal
Introduction
menu. All other menu options enables the user
GRETL (Gnu,Regression, Econometrics and
to customize and use the database to obtain
Time Series Library) is an an open source,
descriptive as well as inferential results
sophisticated, cross platform, flexible, user
contained in the dataset. The most frequently
friendly, accurate and extensible econometric
used menu options for a beginner are file,
software package. The Gretl code base
tools, variable and model options.
originally
Data files
derived
from
program
ESL
(Econometrics Software Library) written by
Gretl has its own native file formats. The basic
Professor Ramu Ramanathan of the University
data format is one in which we use the suffix
of California, San Diago. Gretl as an open
.gdt a dataset that is stored in Extended
source software have been developed through
Markup Language(XML). The system data
numerous developers of free and open source
files have the suffix .dtd which is installed in
software. Richard Stalman of Free Software
the system data directory. In addition to this
Foundation adopt it as a GNU program after
one can import data files in spreadsheets,
its finalization. As a cross platform software,
SPSS, STATA files,Eviews workfiles, SAS
Gretl program is compatiable to operating
export files, plain text files etc. Likely, huge
systems like Linux, MS Windows, and Mac
databases that are available online can also be
OSX. The installation of Gretl in MS windows
accessed with a database handling routine.
is
These online databases can be accessed
just
a
matter
of
downloading
gretl_instal.exe and running the program.
through menu item File-Databases. The most
The Main Window Menus
common
Reading from left to right of main window
(Regression Analysis of Time Series). One can
menu bar, we can see file, Tools, Data, View,
also create a dataset from scratch by opening
Add, Sample, Variable, Model and Help
file-Newdata Set (Cntl +N). The subsequent
options. Thus one can simply start working on
windows will prompt you the steps in creating
Gretl by opening data or database from the file
your own dataset. While doing so the
Government College, Attingal
online
database
is
RATS-4
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
programme will ask you to specify the nature
Using the menu option “Data- Datastructure”,
of your dataset. On the basis of nature, the data
the user can change the nature of the dataset or
set may be
database.
1. Cross sectional Data
Practice 1: Building a Regression model in Gretl
2. Time Series Data
(using sample data files of Ramanathan)
3. Panel Data
Procedure
Cross sectional Data is a collection of
1.
observations (behavior) of multiple subjects
Gretl or Click Shortcut icon created in
(entities) at a single point of time. For example
Desktop): Gretl Opens
maximum temperature, humidity and wind
2.
(behaviours)
select the tab named Ramanathan : The whole
in
Thriruvananthapuram,
Ernakulam and Wayanad (entities) on 1st
Open Gretl (Start- All Programmes-
Go to File- Open Data- Sample files –
Ramanathan data files will be listed.
January,2020.
Time
Series
Data
is
a
collection
of
observartions (behaviours) for a single subject
(entity) at equally spaced different time
intervals. For example maximum temperature,
humidity and wind (behaviours) in Wayanad
town (Single entity) on the first day of every
year starting from 2010 to 2020.
3. Select the dataset named disposable income
Panel Data or Longitudinal data which is also
and consumption ( A double click enables
called as cross sectional time series data is a
opening of the dataset): The data set opens
collection of observations (behaviours) for
multiple
subjects
(entities)
at
multiple
instances (Time). For example the maximum
temperature, humidity and wind (behaviours)
in Thriruvananthapuram city, Ernakulam town
and Wayanad town (multiple entities) on the
first day of every year starting from 2010 to
2020 (multiple time period)
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
This file contains the classic econometric
consumption function. The data window above
displays the current data file, the variable ID, the
variable name, a brief description tag and the range
of data.
4.
To build a simple regression model,
select model from menu bar- from the options
in model menu click OLS (Ordinary Least
Squares) : a specify model window appears as
The output window shows the model based on
given below.
the data given and its various descriptive and
inferential statistics. The window also contains
menus that allow the user to inspect or graph
the residuals and fitted values and to run
various diagnostic tests on the model.
5.
Select Ct (Consumption) as dependent
variable and Yt (Disposable Income) as
Repressor
variable (Independent Variable)
using the arrow marks in the respective boxes.
Then click OK. The window displaying the
regression output will appear as given below.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
PERFORMANCE EVALUATION OF ENTREPRENEURSHIP
DEVELOPMENT SCHEMES OF NATIONAL HANDICAPPED AND
FINANCE DEVELOPMENT CORPORATION
Dr. SHANIMON S
Assistant Professor
Government College, Attingal
Abstract
The growth and development of all economies highly depend on entrepreneurial activity.
Entrepreneurs are the nerves of economic development as they provide a source of income and
employment for themselves. They create an atmosphere of employment generation for others;
produce new and innovative product and services. Entrepreneurial supportive environments are
essential for entrepreneurship development and are evolving all around the developing economies.
An idea of entrepreneurial environment has five metrics, such as easy access to funding,
entrepreneurial culture, entrepreneurial supportive regulatory measures, entrepreneurial supportive
mechanism and entrepreneur friendly policies.
The public and private sector have an equal role
to the development of entrepreneurial eco-system. There are four factors necessary for
entrepreneurial opportunities such as factor-driven entrepreneurship, efficiency-driven
entrepreneurship, innovation-driven entrepreneurship, and necessity-driven entrepreneurship.
Entrepreneurship has been considered as the backbone of economic growth. The level of economic
activities of a country largely depends on the level of entrepreneurial activities in that country.
Entrepreneurs are not born but can be created and nurtured through appropriate interventions in the
form of entrepreneurship development programmes. In the modern competitive world a number of
opportunities emerged from the evolving Information Technology Revolution. A large part of the
population generally lags behind in taking advantage of emerging IT revolution. Therefore, there is a
need to provide skill development through entrepreneurship development to such people in order to
bring them to mainstream of economic development.
Key Words: Entrepreneurship, Economic Development.
1.1 Introduction
of the socially disadvantaged groups especially
Entrepreneurship development programmes
should be designed to upgrade existing skills
and to create new skills by organising various
technical training courses to the mainstream
society.
Specific tailor-made programmes
should be designed for the skill development
for
women,
scheduled
persons
tribes
and
with
disabilities,
scheduled
castes.
Entrepreneurship development and training are
the key elements of social and economic
development among socially disadvantaged
groups. To undertake these tasks on regular
basis
a
Government College, Attingal
number
of
national
level
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
entrepreneurship development institutes and
nationalized banks and other specialised
autonomous bodies have been started in India.
institutions.
These institutes are providing assistance in
funding,
entrepreneurship
development
training, research and consultancy services.
National
Handicapped
Finance
and
Development Corporation is one among such
agencies that provide a number of assistance
and programme for the entrepreneurship
development of differently abled persons.
The financial support, the entrepreneurial
development
programmes
and
various
entrepreneurial skill development programmes
are mainly conduced for the economic and
social
development
persons.
of
Through
differently
these
abled
programmes
differently abled entrepreneurs are able to open
and operate their own ventures. NHFDC is
1.2 National Handicapped and Finance
specialised
Development Corporation - Profile
entrepreneurship development especially for
National
Handicapped
and
Finance
Development Corporation is the apex level
institution promoted by Ministry of Social
Justice & Empowerment, Government of
India. The corporation was incorporated as a
Company on 1997 under Section 25 of the
Indian Companies Act, 1956 to provide
financial support to handicapped people for
entrepreneurship development. It provides a
number
of
entrepreneurship
programmes
for
development
the
among
Differently-Aabled people. The Corporation is
mainly engaged in financial assistance to
differently abled person with minimum 40% of
disability under
NHFDC
micro
undertakes
finance schemes.
entrepreneurship
development programmes in connection with
disabled
institution
people
with
in
the
the
field
support
of
Government of India. NHDFC has been
actively
engaged
in
organising
entrepreneurship
development
training
programmes
are
to
that
beneficial
the
differently abled persons in India.
1.3 Objectives of NHFDC are as follows:
1. To help and support differently abled
persons in carrying out training and
entrepreneurship
development
programmes.
2. Promoting economic growth and selfemployment ventures for the benefit of
differently abled persons.
3. Providing
persons
financial
with
assistance
disabilities
for
for
the
development of their entrepreneurial
skill.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
4. Providing
loan
to
persons
with
nodal agencies such as State Channelising
disabilities for professional or technical
Agencies (SCAs).
education
1.4.1 Credit Based Schemes
leading
to
vocational
rehabilitation and self-employment.
The credit based schemes include
5. Assisting self-employed persons with
financial assistance to the person with
disability in marketing their products
disabilities fulfilling the eligibility criteria, in
and for efficient management of self-
the form of concessional loans on convenient
employment ventures.
terms for setting up of income generating
6. To serve as the apex national level
body for accelerating the process of
entrepreneurship
activity.
1.4.2 Non Credit Based Schemes
development
Non-credit based schemes are mainly
programmes among differently abled
intended to increasing the entrepreneurial
people in India.
talents of differently abled people. These
7. To provide financial assistance to
differently abled persons to
start
business venture.
schemes are:
1. Grant for conducting or sponsoring the
entrepreneurial
8. To share experience and expertise in
Skill
national
Development”.
through
nodal
agencies.
2. Schemes
9. To provide scholarship assistance to
handicapped students.
under
the
scheme of “„Financial Assistance for
entrepreneurial development across the
frontiers
training
and
Entrepreneurial
for
entrepreneurs
sponsoring
to
conduct
the
various
Exhibitions and Trade Fairs.
1.4 Schemes offered by NHFDC
3. Conducting workshops, seminars and
conferences.
National Handicapped Finance and
The institution extends its activities through
Development Corporation has framed various
various programmes to uplift the socio-
schemes to assist differently abled persons.
economic conditions of differently abled
The schemes mainly involving credit based as
persons in India. The corporation is offering
well as non-credit based activities for the
two types of assistance to differently abled
benefit of differently abled persons. These
persons under credit based and non-credit
schemes are mainly implemented through
based schemes. Under credit based schemes,
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
financial assistance is mainly intended for
16
trading
17
and
agricultural
service
and
allied
sector
activities,
activities,
small
business activities, loan to the purchase of
20122013
20132014
NHFDC
Year Wise Achievements
Table 1.1: Details of Amount sanctioned,
Amount Disbursed And Number of
Beneficiaries ( Credit Based Scheme) as on
31.03.2014
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
19971998
19981999
19992000
20002001
20012002
20022003
20032004
20042005
20052006
20062007
20072008
20082009
20092010
20102011
20112012
13296
8018.51
13371
7581.94
13307
Amount Disbursed
(Credit Based Scheme) as on 31.03.2014
VARIOUS SCHEMES OFFERED BY
Years
6958.99
Figure 1.1: Year Wise Achievements of
1.5 PERFORMANCE EVALUATION OF
SL
NO
13253
Source: NHDFC annual report 2014.
vehicle for commercial hiring, educational
loan, and micro credit schemes.
6921.5
Amount
Amount
Total
Total
Sanctioned
Disbursed
Number of
Number of
(Rs. in
(Rs. in
Beneficiaries
Beneficiaries
Lakh)
Lakh)
Amount Disbursed (in Lakhs)
8000
6000
4000
2000
0
Source: Annual Report NHFDC 2013-2014.
Table (1.1) shows that the details of amount
25.55
11
25.55
11
of loan sanctioned, amount of loan distributed
312.6
811
93.13
230
and the total number of beneficiaries under
458.82
801
576.02
1164
credit based schemes for a period of seventeen
1334.23
3330
1180.88
2645
years from the period of incorporation ( 1997-
1522.6
4075
1283.92
2933
2014). The Corporation aims at extending the
1756.12
4702
1841.31
4498
number of beneficiaries under these schemes
2772.93
5635
2682.04
5565
to achieve its main objectives. The number of
2394.06
4754
1768.55
3282
beneficiaries is increased from 11 to 13307 for
1945.18
3951
2344.17
4765
a time span of seventeen years. The amount of
2728.17
5034
2608.77
4831
loan disbursed is increased from rupees 25.55
3381.62
5416
2830.37
5498
lakh to 7581.94 for a period of seventeen years
4121.82
8159
3028.4
5950
from the inception stage of this institution.
3801.67
6443
3079.59
6032
These results show that the corporation has
3225.66
6007
3183.8
6356
been in a path of development to achieve its
5537.98
10704
5085.78
10625
objectives and actively engaged in organising
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
entrepreneurship
development
programmes
schemes. The programmes are mainly intended
which have been beneficial to differently abled
for promoting economic development among
persons in India.
differently abled persons.
Figure
Figure 1 .2: Loan Disbursed
shows
that
the
regression
coefficient of 177.4 with R2 value of 0.697,
(Credit Based Scheme) as on 31.03.2014
12000
which shows that the regression coefficient is
higher in terms of amount disbursed. The
y = 177.4e0.243x
R² = 0.697
10000
(1.2)
growth rate is very high in term of amount of
loan disbursed. The institution specialised in
8000
the field entrepreneurship with the support of
6000
Government
4000
Governmental
of
India
Agencies.
and
The
other
Non-
institution
extends its activities through various credit
2000
based schemes and non-credit based schemes.
0
0
5
10
15
20
Figure 1.3: Total Number of Beneficiaries
(Credit Based Scheme) as on 31.03.2014
Source: NHDFC annual report 2014.
Number of Beneficieries
National Handicapped Finance and
Development
Corporation
is
the
apex
institution in entrepreneurship development
among
differently
abled
people.
20000
10000
0
The
corporation has specialised in credit based and
non-credit based assistance to differently abled
people. The main aim of the institution is to
provide financial support to handicapped
people for entrepreneurship development. The
institution provides number of programmes for
the
entrepreneurship
Differently-Aabled
support
and
development
people.
entrepreneurial
The
among
financial
development
programmes are offered through various
Source: Annual Report NHFDC 2013-2014
The Corporation aims at extending financial
and entrepreneurial assistance to beneficiaries
under credit based and non-credit based
schemes for the achievement of its objectives.
During
the
period
of
2013-2014,
the
corporation attained a highest target, the
number of beneficiaries during the period was
13307 (Table 1.1). The year wise analysis
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
shows that the in last seven year (2007-2014)
have received the amount of loan to start their
the number of beneficiaries all over India was
business ventures. The above data shows that
more than 5000. In the initial period the
the number of beneficiaries who have received
number of beneficiaries was negligible, during
financial assistance from NFDC from the
the inception stage of this institution, the
period of 1997 to 2014 was increased from 11
number of beneficiaries was very stumpy. The
to 13307 with a time span of seventeen years.
corporation attained its objectives through a
In the initial period the number of beneficiaries
long
was negligible. During the period of 2013-14
period
in
terms
of
coverage
of
beneficiaries under various schemes of the
the number of beneficiaries was 13307.
Corporation. During the long lasting years the
STATEWISE ACHIEVEMENTS
Table 5.2: Projects Sanctioned &
Disbursement made up to 31.03.2014
corporation has attained a remarkable growth
in the area of entrepreneurship development
among differently abled persons both in the
number of beneficiaries and amount of
Figure 1.4: Year Wise Data of Total
Number of Beneficiary
(Credit Based Schemes) as on 31.03.2014
20000
2.01
15000y = 56.30x
R² = 0.855
10000
5000
0
Figure
(1.4)
5
10
shows
15
that
the
20
regression
coefficient 56.30 with an R2 value of 0.855,
which shows that the regression coefficient is
higher
in
terms
of
total
State
Andhra
Pradesh
2 Assam
3 Bihar
4 Chandigarh
5 Chattisgarh
6 Delhi
7 Goa
8 Gujarat
9 Haryana
Himachal
10
Pradesh
Jammu &
11
Kashmir
12 Jharkhand
13 Karnataka
14 Kerala
Lakshadwee
15
p
Madhya
16
Pradesh
17 Manipur
18 Maharashtra
19 Meghalaya
20 Mizoram
21 Nagaland
22 Orissa
23 Puducherry
24 Punjab
25 Rajasthan
26 Sikkim
27 Tamil Nadu
1
financial assistance (Table 1.1).
0
S
L
NO
number
of
beneficiaries. The growth rate is very high in
term of total number of beneficiaries, who
Government College, Attingal
Amount
Amount
Sanctioned Number of Disbursed Number of
(Rs. in Beneficiaries (Rs. in Beneficiaries
Lakh)
Lakh)
3218
1429.06
4636
962.81
176.28
10
88.38
2849.34
250.65
54.03
2154.01
5251.46
323
81
358
2267
881
41
5490
10079
169.78
5.5
88.38
2548.77
225.60
54.03
2110.88
5083.62
2196.41
2415
2191.46
967.69
1123
960.88
193.06
1068.05
2323.41
142
3491
3256
193.06
1051.81
2257.21
94.94
122
94.56
2702.31
4178
2085.89
5.49
10047.55
307.50
50
243.62
1359.66
1840.29
852.59
2816.14
51.3
6068.99
41
11321
530
178
501
3081
3259
1283
4473
97
22967
4.49
8036.18
307.50
50
243.62
1239.55
1808.23
829.85
2783.89
51.3
6013.84
302
29
358
2210
867
41
5307
9815
2414
1116
142
3360
3038
122
3577
31
10281
530
178
501
2621
3209
1257
4445
97
22593
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
28 Tripura
Uttar
29
Pradesh
30 Uttarakhand
West
31
Bengal
248.31
213
247.36
2752.72
5545
2711.45
1084.62
2082
1072.85
721.16
2003
668.86
212
5404
offtake
from
the
corporation
and
loan
distributed among the beneficiaries (Figure
2079
1634
1.5).
Table 1 .3: EDP Grant Sanctioned &
Disbursement. For the Year (2013-2014)
Source: NHDFC annual report 2014.
Table (1.2) shows that the state wise details of
projects sanctioned
and the
amount
of
disbursement made up to 2014. The status of
the Corporation has continually improved on
project sanctioning and the disbursement of
loans to beneficiaries over the past years.
Figure
1.5:
State
wise
list
of
Fund
Sanctioned to Differently Abled People
State Wise List of Fund
Saanctioned to Beneficiaries
Rajasthan
Manipur
Haryana
Andhra Pradesh
Amount Disbursed
5000 1000015000
Amount Sanctioned
Axis Title
0
Source: Annual Report of NHFDC (20132014).
Source: Annual Report NHFDC 2013-2014.
Table (1.3) shows that the amount of
Annual disbursement of loans for the benefit
of persons with disabilities in the past years
shows that the corporation was totally focused
on
entrepreneurship
development
among
differently abled persons. The top three states
in terms of loan offtake from the Corporation
during total period were Maharashtra, Tamil
Nadu and Haryana. Bihar, Mizoram and Goa
were the least performed states in terms of loan
grant sanctioned the amount of grant disbursed
by the corporation on 2013-2014. The grant
was
sanctioned
for
entrepreneurship
development training and skill development to
21 States in India. The state of West Bengal
utilized 50 per cent of the grant and offers
training
facilities
to
entrepreneurship
development. Two states namely state of
Kerala and Tamil Nadu did not provide
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
training facilities to anyone. These two state
participating in trade fairs and exhibitions at
have been sanctioned Rs: 735508.56 lakh for
local, state, national and international levels
each for EDP training
through various market assistance schemes.
The Corporation is
Figure 1.6: EDP Grant Sanctioned &
Disbursement. For the Year (2013-2014)
mainly
engaged
in
financial assistance to differently abled person
with minimum 40% of disability under micro
12000000
10000000
Amount…
y = 12717x
R² = -0.35
8000000
finance
schemes.
NHFDC
undertakes
entrepreneurship development programmes in
6000000
connection with nationalized banks and other
4000000
specialised institutions. The financial support,
2000000
the entrepreneurial development programmes
0
and various entrepreneurial skill development
programmes are mainly conducted for the
economic
and
social
development
of
differently abled persons.
Source: Annual Report, NHFDC 2014.
Findings
Conclusion
National
Handicapped
and
Finance
Development Corporation is focusing on
quality skill development on entrepreneurship
development for the well being of differently
abled persons. It provides special emphasis to
attract person with disabilities to skill and
entrepreneurship development programmes. A
number of training facilities are offered to the
target group. Till date, the Corporation has
organized
a number of entrepreneurship
development
programmes
and
skill
development trainings in all states covering a
large number of differently abled persons. The
Corporation
assists
the
beneficiaries
in
1.
National Handicapped Finance and
Development Corporation is conscious about
quality enhancement through entrepreneurship
development among differently abled persons.
2.
The Corporation is focusing on quality
skill development
and providing special
emphasis to attract person with disabilities to
skill
and
entrepreneurship
development
programmes. An entrepreneur may not be able
to succeed without entrepreneurial skills and
qualities, in the modern competitive market
environment.
3.
The intended training facilities are
offering to the target group. Till date, the
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Corporation has organized a number of
Reference
entrepreneurship
1.
Noel J. Lindsay, Wendy A. & Fredric
Kropp, (2009). Start-up intentions and
behavior of necessity-Based entrepreneurs A
longitudinal
study,
Frontier
of
Entrepreneurship Research, 1-5.
2.
North, (1990). A Transaction Cost
Theory of Politics. SAGE Journals, Vol. 2,
Issue 4.
3.
O‟ Brein et al (1997), Poverty and
Social Exclusion in North and South, IDS
Working Paper 55. Institute of Development
Studies and Poverty Research Unit, University
of Sussex, Brighton.
4.
Peter F. Drucker (1970), Practice of
Management, Allied Publishers, New Delhi.
5.
Rakesh Gupta,
& Ajay Pandit (2013). Innovation and growth
of small and medium enterprises: role of
environmental dynamism and firm resources
as moderating variables. International Journal
of
Entrepreneurship
and
Innovation
Management, 17, (4/5/6), 284 – 295.
6.
Russell, W. Teasley., Richard, B. &
Robinson, (2005). Modeling knowledge-based
entrepreneurship and innovation in Japanese
organizations. International Journal of
Entrepreneurship, 9, 19-144.
7.
Saadat Saeed, Moreno Muffatto,
Shumaila Y. Yousafzai, (2014). Exploring
intergenerational influence on entrepreneurial
intention: the mediating role of perceived
desirability and perceived feasibility of
Internationaisationl
Journal
of
Entrepreneurship and innovation management,
18, 2/3, 134 – 153.
8.
Sahalman (1987). Value creation in
place management: The relevance of Service
Providers.
International
Journal
Of
Management
Science
and
Business
research,3(11), 13-19.,
development
programmes
and skill development trainings in all states
covering a large number of differently abled
persons.
4.
During the year last twenty years
entrepreneurship
and
skill
development
development
programmes
trainings
were
organized in all states covering thousands of
persons with disabilities.
5.
The
Corporation
assists
the
beneficiaries in participating in trade fairs and
exhibitions at
local, state, national and
international levels through various market
assistance schemes.
6.
The Corporation provides the space for
reimbursement
cost
of
accommodation
expenses
travelling
and
and
providing
carriage cost of goods and daily allowances for
the beneficiary and escort for participation in
these fairs.
7.
The beneficiaries are getting the
chance of market opportunities through their
participation
in
these
events.
Such
participation also showcases the abilities of the
differently abled for the awareness of general
public.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
TRENDS IN GLOBAL AQUACULTURE PRODUCTION
Dr. ANITHA S.
Associate Professor
Government College, Attingal
Abstract
Aquaculture is the farming and husbandry of aquatic creatures under regulated or semi-regulated
environmental conditions. These organisms may be fishes, crustaceans, molluscs, aquatic plants and
animals. Global Production from capture fisheries showed a declining trend in the past few decades.
Despite the mechanism of fishing techniques, catch per unit effort declined and unit cost of
production increased. The basic reason for the declining rate of growth in fish production is
attributed to over exploitation of scarce fishery resources. This necessitated a shift on emphasis from
development of capture fisheries to development of culture fisheries. Scientific aquaculture- a bio
technology to boost fish production through fish culture has become popular in major fish producing
countries of the world. This research paper analyses the gloabal trends in aquaculture production
based on the valid database of Fisheries Global Information Systems (FIGIS).
Key Words : Aquaculture, Capture Fisheries, Global trends
1.1 Introduction
security and livelihoods, the Thirty-first
Global Production from capture fisheries
Session of the FAO Committee on Fisheries
showed a declining trend in the past few
(COFI) endorsed the convening of the Global
decades. Despite the mechanism of fishing
Conference on Inland Fisheries: Freshwater,
techniques, catch per unit effort declined and
Fish and the Future (26–28 January2015). The
unit cost of production increased. The basic
conference was part of a memorandum of
reason for the declining rate of growth in fish
understanding between FAO and Michigan
production is attributed to over exploitation of
State University, and brought together about
scarce fishery resources. This necessitated a
200
shift on emphasis from development of capture
representatives from civil society from around
fisheries to development of culture fisheries.
the globe. The Session accepted the following
Scientific aquaculture- a bio technology to
Ten steps to Responsible inland aquaculture.1.
boost fish production through fish culture has
Improve
become popular in major fish producing
production
to
enable
countries of the world. In recognition of the
management
2.
Correctly
vital role inland fisheries play in global food
aquatic ecosystems 3. Promote the nutritional
scientists,
Government College, Attingal
the
resource
assessment
managers
of
and
biological
science-based
valued
inland
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
value of inland fisheries 4. Develop and
are the leading importers of fish and fisheries
improve science-based approaches to fishery
products.
management
among
5.
Improve
freshwater
users
communication
6.
Improve
1.3.
Trends
in
Gloabal
Aquaculture
Production
governance, especially for shared water bodies
7. Develop collaborative approaches to cross-
The table below shows the figures in MT
sectoral integration in development agendas 8.
(Million
Respect equity and rights of stakeholders 9.
production from 1997 to 2016
Tons)
Make aquaculture an important ally and 10.
Develop an action plan for global inland
fisheries
1.2. Aquaculture – Global Scenario
The figures from FAO FIGIS records show
that 47 percent of the worlds‟ total fish
production (80.4 /169 Million Tons) is through
aquaculture. The percent of Aquaculture to
Total fish production shows an increasing
trend from 35 percent in 2007 to 47 percent in
2016. The figures of marine fishing shows
decreasing trend from 65 percent to 53
percent.89.3 percent of the world aquaculture
production ( 68.3/76.6 Million Tons) is from
the continent Asia. The average growth rate is
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
of
gloabal
aquaculture
Aquaculture Production
(World) in MTs
30.23
30.25
31.59
32.41
33.24
36.78
38.9
41.9
44.2
47.25
49.9
52.9
55.7
59
61.8
66.5
70.3
73.7
76.6
80.4
Source : FIGIS (FAO)
4.4%. The second leading producer is America
( 3.2/80.4 MT). Finfish is the major item in the
aquaculture product group (52 /76.6 MT). 52.5
% of the world export of fish and fisheries
products is from Ten countries including India.
The share of China, the leading exporter is
14% and that of India is 3.7%. USA and Japan
Table shows global inland fish production
from the year 1997-98 to 2015-16. During
these 20 years, the total inland fish production
increased to 80.4 million tons from 30.23
million tons. In every year, there is an increase
in the production quantity is seen.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Figure 3.2. Global Aquaculture Production Trend
global aquaculture production for the 20 years
from 1997 to 2016
under review. The regression model reveal that
the time series trend is linear and increasing
annually at the rate of 2.7781 Million tons.
Even though the model fitness indicator R2
shows higher value, the validity of the model
need to be tested for all assumptions in
Ordinary
Least
Squares.
Thus
a
pure
econometric time series analysis may improve
the model through various tests of validity.
Reference
The world aquaculture production increased
from 30.23 million tons to 80.4 million tons
over a period of 20 years from 1997 to 2016.
The data over this period seem to show a
linear
growth
in
global
aquaculture
production. Using the function y = a+bx, the
equation estimated using regression tool,
results in b = 2.7781, a = 0.5523, which is
2
valid with a significant R value = 0.978.Thus
in every year the a marginal increase in
production is 2.7781 Million Tons. The
M.Krishnan and P.S.Birthal (Eds.),
(2010). Aquacultural Development of
India: Problems and Prospects, New
Delhi, National Centre for Agricultural
Economics and Policy Research.
Meade, James, W. (1998). Aquaculture
Management, New Delhi, CBS Publishers
& Distributors.
Meehan, W.E. (2002). Fish Culture in
Ponds and Other Inland Waters, Pilani,
H.R.Publishing House.
Menon, K.M. (1998). Matsyakrishi
(Malayalam), Thiruvanthapuram, Kerala
Bhasha Institute.
Michael, B.N., Valenti,W.C., Tidewell, J.
H., D‟Abramo, L.R. &Kutty, M. N.
validity of this model need to be tested for
various assumptions
in the time series
modeling.
1.4. Conclusion
The simple linear regression model explained
in this paper attempts to derive the trend in
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
PERFORMANCE EVALUATION OF SBI LIFE INSURANCE COMPANY
ANSA S.
Research Scholar, Reserach & P.G.Department of Commerce, Government College, Attingal,
University of Kerala,ansaismail@yahoo.com,9446108234
Abstract
Insurance is a protection against financial loss arising on the happening of an uncertain event and also
serves as a tool for capital formation. Bancassurance means selling the insurance products through the
banking network. While in the initial stage, the SBI Life Insurance Company act as a bancassurance
channel, now it is developing its own agency for selling insurance products. With a wide network of
908 offices span across the country SBI Life Insurance Company has a mission to emerge as the
leading insurer
by offering variety of life insurance products, pension schemes, ensuring high
standards of customer service and better operational efficiency. The company shows a tremendous
growth during the last two decades with an upward trend in the net profit after tax and net worth.
Keywords: life insurance, SBI Life, premium
Introduction
large number of life insurance companies
Life insurance policies are a safeguard against
the uncertainties of life. In life insurance, the
insured transfers a risk to the insurer by paying
an amount called premium in exchange.
Insurance is a protection against financial loss
arising on the happening of an uncertain event
and serves as a tool for savings and
investment. In India, the life insurance has its
functioning in India. From the last two
decades, commercial banks were entered into
the insurance sector as a distribution channel
for insurance products. There are so many tieups and joint ventures between banks and
insurance
companies
were
started
for
marketing the insurance products.
SBI Life Insurance Company was
insurance
incorporated on 11th October 2000 as a joint
company established at Kolkata. Now a day,
venture between SBI and BNP Paribas Cardif.
life
insurance industry in India has a
62.1% of total capital is owned by SBI and
predominant place in the economy. There are a
22% owned by BNP Paribas Cardif. The other
origin
from
the
oriental
life
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
investors are Value Line Pvt..Ltd. and Mc
an insurance plan. The company aims to gain
Rittchie Investment Pvt.Ltd. holding 1.95%
its competitive advantage through customer
and remaining 12% with public. The company
centric approach.
has an authorized capital of Rs.20 billion and a
paid up capital of Rs.10 billion. It is one of the
leading private life insurance companies that
Objective of the study
The study has the following objective.
1. To analyze the performance of SBI
Life Insurance Company.
offer a wide range of insurance products
through its strong distribution channel. The
majority shares
of SBI
Life
Insurance
Company are owned by SBI. SBI Life has a
unique multi-channel distribution network
comprising
an
expansive
Methodology
bancassurance
channel with SBI, its largest bancassurance
partner in India with their individual agents
networks comprising 108261 agents as on 31st
March 2018 as well as other distribution
channels including brokers, corporate agents,
direct sales or other intermediaries. While in
The study describes the growth and
performance of SBI Life Insurance Company
using secondary data. The required data were
collected from the annual report of SBI Life
insurance Company and other journals. For
analyzing the performance of SBI Life, data
were collected for a period of 12 financial
years from 2008 to 2019.
PERFORMANCE EVALUATION OF SBI
LIFE INSURANCE COMPANY
TABLE 1: GROSS WRITTEN PREMIUM (Rs.in billion)
the initial stage, the SBI Life Insurance
Company act as a bancassurance channel, now
it is developing its own agency for selling
insurance
products.
SBI
Life
Insurance
Company has a wide network of 908 offices
spread across the country with 14,961
employees. It has also tie-ups with 76
corporate agents, 17 bancassurance partners
and 99 brokers along with 184452 trained
insurance personnel, catering wide range of its
customers. SBI Life offers innovative and
newer
technologies
to
provide
more
Year
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
First
year
premiu
m
33.35
45.65
62.82
33.9
21.93
26.18
29.98
33.31
46.31
62.07
81.39
90.57
Single
premium
14.57
8.22
7.59
42
43.39
25.65
20.68
21.98
24.76
39.37
28.27
47.35
Renewal
premium
8.29
18.25
30.63
53.56
66.02
52.67
56.73
73.38
87.19
108.71
143.38
191.97
Source: Annual reports of SBI Life Insurance Company
from2008-2019
convenient options to customers for selecting
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
figure 1: Chart showing trends in Gross Written Premium
250
200
y = 13.59x - 27295
R² = 0.862
Rs. In billion
150
100
y = 3.656x - 7315.
R² = 0.350
50
y = 2.126x - 4254.
R² = 0.320
0
2006
2008
2010
2012
2014
2016
2018
2020
-50
Year
First year premium
Single premium
Renewal premium
Figure one portraits Gross Written premium
annual increase of Rs.13.59 billion with a good
during the period 2008–2019. The first year
explanation of 86.2%. Thus all Gross written
premium increased from Rs.33.35 billion to
premium shows linear trends with sufficient
Rs.90.57 billion in 2019. The trend line shows
explanation to the model.
an annual linear increase of Rs.3.656 billion
with an explanation of 35% (R2 =0.35). Likely,
the Single premium for 2008 raised from
Rs.14.57 billiontoRs.47.35 billion in 2019
which shows an upward trend line with an
annual linear increase of Rs.2.126 billion with
an explanation of 32% (R2=0.32).Similarly,
the renewal plan also increased from Rs.8.29
billion in2008toRs.191.97 billion in 2019. The
trend line clearly shows a linear trend with an
TABLE 2:PROFITS AND NET WORTH
(Rs.in billion)
Year
2008
2009
2010
2011
2012
2013
2014
2015
Government College, Attingal
Profit after tax
0.34
-0.26
2.76
3.66
5.56
6.22
7.4
8.2
Networth
10.07
9.78
12.65
16.3
21.56
27.1
33.42
40.39
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
2016
2017
2018
2019
8.61
9.55
11.5
13.27
47.33
55.52
65.28
75.76
last 12 years. The company has only a profit of
0.34 percent during 2008 and it increased to
13.27 percent during 2019. Its trend line shows
Source: Annual reports of SBI Life Insurance Company
from 2008-2019
upward trend
with an annual increase of
1.1519
an
with
explanation
of
97%
Figure 2: Chart showing trends in Profit after Tax and Networth
80
70
y = 6.084x - 12216
R² = 0.957
60
Rs. In billion
50
40
30
y = 1.151x - 2313
R² = 0.973
20
10
0
2006
-10
2008
2010
2012
2014
2016
2018
2020
Year
Profit after tax
Figure two portraits net profit after tax and net
Networth
(R2=0.9739). The net worth also increased
worth of SBI Life insurance Company for the
from 10.07 percent on 2008 to 75.76 percent
on 2019 which shows an upward trend line
with an annual linear increase of 6.0844 with
in India. It offers variety of life insurance
an explanation of 95.7% ( R2=0.9572). Thus,
products through its multi distribution channel.
the profit and net worth shows a high rate
The company shows a tremendous growth
growth for the last 12 years.
during the last two decades. The gross written
Conclusion
premium of company shows an increasing
SBI Life Insurance Company is placed as
trend. The renewal premium increased at high
pioneer to the development of bancassurance
rate during the last 12 years.The net profit after
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
tax and net worth of the company is shows an
upward trend. The company gets more profits
and net worth on 2019 as compared to
2008.The company attained its competitive
advantage by offering more products and
services on customer centric. It has a large
network of branches and individual agents
spread all over India for distributing insurance
products. SBI Life attained its operational
efficiency by focused more on rural customers
and thereby increased the standard of living
and development of society as a whole.
References
1. Agrawal.A (2004).Distribution of life
insurance products in India, Insurance
Chronicle, September, p.24.
2. Sinha (2005).Bancassurance in India,
The Insurance Times, December, p.34.
3. Okeahalam (2008). Success factors for
bancassurance, Journal of Banking and
Finance, 8,pp.22-28.
4. www.sbilife.co.in
5. Annual reports of sbi life insurance
company
6. www.irdai.gov.in
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
ARIMA MODEL IN PREDICTING NSE NIFTY50 INDEX
Dr. LAKSHMANAN M.P
Assistant Professor
PG Department of Commerce
Government College Chittur
Email:mpl77lic@gmail.com
Abstract
The prediction of stock prices and related indices is of vital importance in the field of economics and
business and many research works has been carried out over the years to develop predictive models.
The historical data on index closing price was used to develop several ARIMA (Autoregressive
Integrated Moving Average) models by using Box-Jenkins time series procedure and the adequate
model was selected according to four performance criteria: Akaike Criterion, Schwarz Bayesian
Criterion, Maximum Likelihood and Standard Error. The paper presents the process of building stock
price predictive model using ARIMA Model. Published stock data obtained from NSE (National
Stock Exchange) is used with stock predictive model developed. Therefore, Monthly data from
January 2001 up to December 2019( 228 observations) is used for this study. The results obtained
revealed that ARIMA model has high potential in short run prediction and will be helpful to investors
in stock market.
Key Words: Time series, ARIMA Model, Stock/Index Price Prediction, Short term Prediction.
INTRODUCTION
difficult task in financial forecasting due to
Prediction of stock/index prices are always an
varied reasons especially its complex nature,
interesting area of research because of its
high amount of volatility, influence of global
peculiar characteristics like volatility distinct
market forces etc. Any investor will try to
from other financial products
depend on a forecasting method that could
in financial
market. In the information and technology era,
guarantee
individuals
investment risk from the stock market. This
and
institutions
are
highly
as
easy
and
motivating
minimize
empowered to make investment decisions and
stands
design effective strategies as to their daily and
researchers in evolving and developing new
future financial requirements. The prediction
predictive models. The Nifty 50 is an indicator
of stock/index prices is one of the most
of the top 50 major companies on the NSE.A
Government College, Attingal
major
profiting
factor
for
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
large number of methods have been used for
models are sed in time series data to predict
NSE including AR (Autoregressive model),
future points in the series. Such models are
ARMA (Autoregressive
Average
applied in cases where data is non-stationery
Model), ARIMA (Autoregressive Integrated
wherein differencing can be done to reduce the
Moving Average Model) and so on. But
non-stationarity.
ARIMA is most widely used on among them.
models are generally denoted ARIMA (p, d, q)
Stock market price may be of opening price,
where parameters are non- negative integers
lowest price, highest price, adjusted closing
then p, d, q refer to the autoregressive,
price and volume. The study takes into account
differencing, and moving average terms for the
closing stock price (in Rs). The analysis of
non-seasonal component of the ARIMA
stock data has been done using SPSS 20
model. Seasonal ARIMA models are usually
Software and Gretl and E Views 8
denoted ARIMA (p, d, q) (P, D, Q)m, where m
Moving
Non-seasonal
ARIMA
refers to the number of periods in each season,
LITERATURE REVIEW
and
The major works using ARIMA model in the study
of stock market data are reviewed .Banerjee, D.
(2014) applied ARIMA model to forecast in
Indian Stock Exchange the future stock
indices. Paulo Rotela Ju-nior et al. (2014)
described ARIMA model to obtain short-term
P,D,Q
refer
to
the
autoregressive,
differencing, and moving average terms for the
seasonal
component
of
the
ARIMA
model.Box-Jenkins method./approach has been
used for analysis and modeling the time series.
This methodology comprises the following
steps.
forecasts to minimize prediction errors for the
Bovespa Stock Index. Renhao Jin et al. (2015)
used ARIMA model to predict in Shanghai
Composite Stock Price Index . All the studies
were based on closing stock price.
(a) Identification of model: -This stage
involves finding whether the time series data is
stationary or not and compare the estimated
Autocorrelation Function (ACF) and Partial
Autocorrelation Function (PACF) to find a
OBJECTIVE OF THE STUDY
To forecast the closing stock price of NSE
NIFTY50 using time series ARIMA Model
match.
(b) Estimation of Parameters(coefficients): -
DATA & METHODOLOGY
Estimating the parameters for Box Jenkins
An ARIMA Model is a generalisation of an
models is a complicated nonlinear estimation
ARMA model in time series analysis. These
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
problem. The main approaches for fitting Box
variable
- Jenkins models are nonlinear least squares
Forecasting may also be based on expert
and
estimation.
judgments, which in turn are based on
Parameter estimates are usually obtained by
chronological data and experience. When
maximum likelihood which is fit for time
model selected is found satisfactory during the
series.
analysis, it can be used for forecasting
maximum
likelihood
Estimators
efficient,
and
are
always
consistent
for
sufficient,
Normal
or
other
associated
variables.
purpose.
distribution.
ARIMA model uses the historic data and
(c) Diagnostic checking (verification): -The
decomposes it into AR ( Auto Regressive) –
diagnostic checking is pre-requisite to ensure
indicates weighted moving average over past
the appropriateness of the selected model.
observations, Integrated (I) –indicates linear
Selection of particular model can be done
trends or polynomial trend and moving
based on the values of certain criteria like log
average
likelihood, Akaike Information Criteria (AIC)/
average over past errors. As such it has three
Bayesian Information Criteria (BIC)/ Schwarz-
model parameters AR (p), I(d) and MA(q) all
Bayesian Information Criteria (SBC). After
combined to forming ARIMA (p,d,q) model
model selection, its o be verified that whether
where p represents order of auto correlation, d
estimated model is satisfactory or not by
represents order of integration (differencing)
studying the pattern among the residuals if
and q represents order of moving averages.
(MA) –Indicates weighted moving
there any. The values of ACF may be checked
to see that whether the series of residuals is
white-noise. After fitting tentative model to
RESULTS & DISCUSSION
The descriptive statistics of the NSE Nifty Fifty
data for the analysis period is tabled below.
data, diagnostic checks are done and overall
adequacy of the model selected can be known
by examining a quantity Q known as LjungBox
statistic
that
follows
Summary statistics, using the observations 2001:01
- 2019:12 for the variable 'Price' (228 valid
observations)
chi-square
distribution.
Table 1 Descriptive Statistics-Price
N
(d) Forecast. It means prediction of values of a
Stati
stic
228
Range
Mini
mum
Maxi
mum
Mean
Std.
Deviatio
n
Statistic
11254.6
000
Stati
stic
913.8
500
Statis
tic
12168
.4500
Statisti
c
5366.96
5570
Statistic
3210.25
36370
variable based on identified past values of that
Government College, Attingal
Skewness
Stati
stic
.365
Std.
Error
.161
Kurtosis
St
d.
Stati
Er
stic
ror
-.869
.32
1
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Figure (1) depicts the original pattern of the
series to have general overview whether the
time series is stationary or not and it can be
seen that time series is not stationary( i.e. has
random walk pattern).
PRICE
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
2002
2004
2006
2008
2010
2012
2014
2016
2018
Figure (1)Graphical presentation of the NSE Nifty
50 Price Index
Figure (2) The correlogram of NSE
Nifty 50 Stock Price Index
X axis represents trading years and Y axis
represents stock index
price. Figure (2) is the
correlogram of NSE Nifty 50 time series. The ACF
the graph , it is seen that ACF dies down slowly
which simply means that the time series is nonstation to stationery. When series is not stationery,
it
is
converted to a
stationery series
by
Figure (3) The ACF diagram of NSE
Nifty 50 Stock Price Index
differencing. After the first difference, the series
Differenced PRICE of
NSE Nifty 50 becomes
stationery as given infigure3 and figure 4 of the
line graph and correlogram respectively.
Figure (4) The PACF diagram of NSE Nifty
50 Stock Price Index
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Differenced PRICE
Figure 8and figure9 of modified series of
1,200
correlation coefficients figures of ACF and
800
PACF shows that there is stationarity in the
400
data
series and most of the values lie within
0
the confidence interval which is validated by
-400
ADF Unit root test result as given in figure 7.
-800
The value of Durbin –Watson (DW) was
-1,200
2002
2004
2006
2008
2010
2012
2014
2016
2018
Figure 5 Graphical presentation of the NSE
NIFTY 50 stock price index after first
differencing.
0.009075 for the sample data of NSE Nifty 50
and same was 2.026736 (for first difference).
The data first differenced is having d value
greater than Du (1.78) as such the null
hypothesis is not rejected and assumed that
there is no auto correlation.
The basic idea of ARIMA model is to view the
data sequence as formed by a Stochastic
Process on time. When the model has been
identified, it model can be used to estimate the
future value based on the past and present
Figure 6 The correlogram of NSE Nifty 50 stock
price index after first differencing.
value of the time series. Based on the
identification rules
on
time
series,
the
corresponding model can be established. If a
partial correlation function of a stationary
sequence is truncated, and auto-correlation
function is tailed, it can be concluded the
sequences for AR model; if partial correlation
function of a stationary sequence is tailed, and
the auto-correlation function is truncated, it
can be strong that the MA model can be fitted
Figure 7ADFUnit root of NSE NIFTY 50 stock
price index after first differencing
for the sequence. If the partial correlation
function of a stationary sequence and the
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
autocorrelation function are tailed, then the
ARMA model is appropriate for the sequence.
Figure 9 The PACF diagram of first order
differencing of NSE Nifty 50 closing stock
price
Figure 8 The ACF of NSE Nifty 50 stock
price index after first differencing.
For the various correlations up to 24 lags are
computed and the same along with their
significance which is tested by Box-Ljung (Q)
test are provided in Table 1 and 2.
AutocorrelationsSeries: DIFF(Price,1)
Lag Autocorrelation Std. Error
a
Box-Ljung Statistic
Value
df Sig.
b
Partial Autocorrelations
Series: DIFF(Price,1)
Lag Partial Autocorrelation Std. Error
1
-.453
.067
2
-.421
.067
3
-.275
.067
4
-.174
.067
5
-.159
.067
6
-.180
.067
1
2
-.453
-.129
.066 47.041
.066 50.897
1
2
.000
.000
7
-.064
.067
3
4
.094
.026
.066 52.960
.066 53.112
3
4
.000
.000
8
-.179
.067
5
-.049
.065 53.670
5
.000
9
-.151
.067
6
-.019
.065 53.756
6
.000
10
-.014
.067
7
8
9
10
.085
-.113
.046
.091
.065
.065
.065
.065
55.465 7
58.467 8
58.980 9
60.965 10
.000
.000
.000
.000
11
.032
.067
12
-.017
.067
13
.007
.067
11
-.055
.065 61.688 11
.000
14
-.138
.067
12
13
14
-.064
.058
-.069
.064 62.678 12
.064 63.500 13
.064 64.673 14
.000
.000
.000
15
-.131
.067
16
-.024
.067
15
16
.034
.077
.064 64.950 15
.064 66.387 16
.000
.000
Table 1 The ACF value of first order
differencing of NSE Nifty 50 closing stock
price
Table 2 The PACF value of first order
differencing of NSE Nifty 50 closing stock
price
Table 3 shows the different parameters of
autoregressive (p) and moving average (q)
among
the
Government College, Attingal
several
ARIMA
Model
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
experimented upon, ARIMA (0, 1,1 ) is
considered the best for NSE Nifty 50 closing
stock price. The model gives the smallest BIC
11.484.
.
(1,1,1)
ARIMA(1,1,2)
ARIMA
(0,1,2)
ARIMA
(3,1,1)
11.539
11.564
11.558
(5,1,0)
ARIMA
(5,1,1)
ARIMA
(6,1,0)
ARIMA
(6,1,0)
11.614
11.642
11.738
The model verification is done by checking the
residuals of the model to observe whether they
contain any systematic pattern which still can
be removed to get better on the chosen
ARIMA. This is done through examining the
autocorrelations and partial autocorrelations of
the residuals of various orders.
Figure 10Correlogram of Residuals of NSE
Nifty 50 closing stock price
Figure 10 is the residual of the series. If the
model is good, the residuals (difference
between actual and predicted values) of the
model are series of random errors. Since there
are no significant spikes of ACFs and PACFs,
it means that the residual of the selected
ARIMA model are white noise, no other
Table no 4 Model Statistics of Nifty 50 closing
stock price
Model
Model Ljung-Box Q(18) Numbe
Fit
r of
statistic
Outlier
s
s
RStatistic D Sig
squared
s
F
.
Price.25
Model_ 0.991
20.303 17
0
9
1
significant patterns left in the time series.
Therefore, there is no need to consider any
AR(p) and MA(q) further.
Table 3 Normalized BIC Values of Nifty 50
closing stock price
ARIMA
(p,d,q)
ARIMA(0,1,0)
Normalized
BIC
12.161
ARIMA(1,1,0)
11.959
ARIMA
(0,1,1)
ARIMA
11.484
11.516
ARIMA
(p,d,q)
ARIMA
(3,1,0)
ARIMA
(4,1,0)
ARIMA
(4,1,1)
ARIMA
Normalized
BIC
11.740
11.738
Table no 5 ARIMA Model Parameters
Esti SE t Si
mat
g.
e
20. 2. .0
Const 47.5
Pric
09 36 1
Pr No
ant
63
e5 7 9
ic Transfor
Mod
e mation Diffe
el_1
1
rence
11.586
11.742
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
SPSS Forecasting is used. Table 6 and Figure
12 present the results of the NSE Nifty 50
share price obtained by applying ARIMA
Model (0,1,1) for the next 7 months from
January 2020to July 2020.
Figure 11 Residual ACF and PACF diagram of
actual Nifty 50
The ACF and PACF of the residuals (Figure
11) indicate `good fit' of the model.
CONCLUSION
Forecasting Share price index is of vital
importance and utility to stock market
investors. The investment decision depends on
the future share prices. In this context , an
ARIMA model to NIFTY 50 index is index is
Table no 6 Forecast NSE Nifty 50 share price
index January 2020 to July 2020
Forecast
Mod
el
Pric
eMod
el_1
For
ecas
t
UC
L
LC
L
229
122
16.0
1
128
12.6
2
116
19.4
0
230
122
63.5
8
131
07.3
1
114
19.8
4
231
123
11.1
4
133
44.5
0
112
77.7
8
developed by using Box-Jenkins Time series
approach. The historical share price data were
used to develop
232
123
58.7
0
135
51.9
2
111
65.4
8
233
124
06.2
6
137
40.3
2
110
72.2
0
234
124
53.8
3
139
15.2
2
109
92.4
4
235
125
01.3
9
140
79.8
7
109
22.9
1
adequate
several models and the
one was selected according to
performance criteria SBC,AIC, Standard Error
and Maximum Likelihood. In the process of
model building , the original Nifty 50 data is found
to be Non stationary. But the first order
differencing of Original Nifty 50 is stationery. In
the study ARIMA (0,1,1) model is developed for
analyzing and forecasting Nifty 50 closing
stock price among all of various tentative
models having lowest BIC values. The study
highlights that influence R square is 99% high
Figure 12 NSE Nifty 50 share price
index, Fit,LCL,UCL and forecasting
and mean absolute percentage error is very
Forecast : After defining the most appropriate
that the prediction accuracy is more in fitting
model of share price , forecasting is to be done
of Nifty 50.
small for the fitted model. Thus it can be seen
and to predict trends and develop forecast IBM
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Acknowledgment:
The
author
expresses
Series ARIMA Models, International
heartfelt gratitude to the authors of articles
Journal of Engineering Research &
cited in references(2,3,9,10) for depending to
Technology (IJERT) Vol. 4 Issue 03,
great extent on the literature and methodology
March-2015.
framework in analyzing and forecasting the
9.Mohammed Ashik& S Kannan K (2017),
Nifty Fifty share price.
“Forecasting National stock price ARIMA
REFERENCES
Model”,Global and Stochastic analysis,
1.D. Banerjee, “Forecasting of Indian stock
Vol 4, No 1, January 2017,77-81
market using time-series ARIMA model,” in
Proc. Conference Paper, ICBIM-14, 2014.
2.BanhiGuha and Gautam Bandyopadhyay,”
10.A A Adebiyi and Charles Ayo (2014),
Stock Price prediction using the ARIMA
Gold Price Forecasting Using ARIMA
Model,2014 UKSim-AMSS 16th
Model”, Journal of Advanced Management
International Conference on Computer
Science Vol. 4, No. 2, March 2016, p117-
Modelling and Simulation Research Gate
121
3.Jamal Fattah, et al , Forecasting of demand
using ARIMA model, International Journal
of Engineering Business Management,
Volume 10: 1–9
4.Shen S and Shen Y. ARIMA model in the
application of Shanghai and Shenzhen stock
index. Appl Math 2016; 7:171–176.
5. Hanke JE and Reitsch AG.Business
forecasting, 5th ed. Englewood Cliffs. 1995.
6.Brockwell PJ and Davis RA. Time series:
theory and method. Berlin: Springer-Verlag,
1987
7.Hamilton JD. Time series analysis.
Princeton: Princeton University Press, 1994.
8. Dr. (Ms.) ShaliniBhawanaMasih, et al ,
Modeling and Forecasting by using Time
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
FINANCIAL DEEPENING AND ECONOMIC DEVELOPMENT OF INDIA
Dr. PRADEEP KUMAR.N
Assistant Professor of Commerce,
Mahatma Gandhi College, Thiruvananthapuram
Abstract
A high level of financial deepening is a necessary condition for accelerating growth in an
economy. This is because of the central role of the financial system in mobilizing savings and
allocating same for the development process. This study examined financial deepening and
economic development in India between 1995 and 2017. The study made use of secondary data,
sourced for a period of 22 years. The two stages least squares analytical framework was used in
the analysis. The study found that financial deepening index is low in India over the
years. It was also found that the nine explanatory variables, as a whole were useful and had a
statistical relationship with financial deepening. But four of the variables; lending rates,
financial savings ratio, cheques cleared/GDP ratio and the deposit money banks/ GDP
ratio had a significant relationship with financial deepening. The study concluded that the
financial system has not sustained an effective financial intermediation, especially credit
allocation and a high level of monetization of the economy. Thus the regulatory
framework should be restructured to ensure good risk management and corporate
governance in the system.
Key Words; Financial Sector, Corporate Governance, Financial Reforms, Financial Savings,
Financial Market, Gross Domestic Product, Financial Deepening
Introduction
The reforms in the financial system in
India which heightened with the 1991
deregulation, affected the level of financial
deepening of the country and the level
relevance of the financial system to economic
development.
However,
the
rapid
globalization of the financial markets since
then and the increased level of integration of
the Indian financial system to the global
system have generated interest on the level of
financial deepening that has occurred.
The financial system comprises various
institutions, instruments and regulators.
According to the Reserve Bank of India
the financial system refers to the set of
rules and regulations and the aggregation
of financial arrangements, institutions,
agents, that interact with each other and
the rest of the world to foster economic
growth and development of a nation. The
financial system serve as a catalyst to
economic development through various
institutional structures. The system
vigorously seek out and attract the reservoir
of savings and idle funds and allocate same
to entrepreneurs, businesses, households
and government for investments projects
and other purposes with a view of returns.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
This forms the basis for economic
development.
The financial system play a key role in the
mobilization and allocation of savings for
productive, use provide structures for
monetary management, the basis for
managing liquidity in the system. It also
assists in the reduction of risks faced by
firms and businesses in their productive
processes, improvement of portfolio
diversification and the insulation of the
economy from the international economic
changes. The system provides the necessary
environment for the implementation of
various economic policies of the government
which is intended to achieve noninflationary growth, exchange rate stability,
balance of payments equilibrium foreign
exchange management and high levels of
employment.
The Indian financial system can
be broadly divided into two sub-sectors, the
informal and formal sectors. The informal
sector has no formalized institutional
framework, no formal structure of rates and
comprises the local money lenders, thrifts,
savingsand loans associations.This sector is
poorly developed, limited in reach and not
integrated into the formal financial system.
Its exact size and effect on the economy
remain unknown and a matter of
speculation. The formal sector, on the
other hand, could be clearly distinguished
into the money and capital market
institutions. The money market is the
short-term end of the market and
institutions here deal on short term
instruments and funds. The capital market
encompasses the institutions that deal on
long-term funds and securities.
The regulatory institutions in the
financial system are the Ministry of
Finance, the Reserve Bank of India as the
apex institution in the money market and
the SEBI as the apex institution in the
capital market
The process of financial sector
reform consists of the movement from an
initial situation of controlled interest rates,
poorly developed money and securities
market and under-developed banking system,
towards a situation of flexible interest
rates, an expanded role for market forces in
resource allocation, increased autonomy for
the central bank and a deepening of the
money and capital markets. The link
between financial sector stability and growth
is, explained by increased market depth,
which potentially increases market efficiency.
It also reduces risks through the elimination
of weak institutions.
Need and Significance of the study
Financial sector reforms seek to
develop an efficient framework for monetary
management. This encompasses efforts to
strengthen operational capacities of the banking
system, foster efficiency in the money and
securities markets, over-haul the payments system
and ensure greater autonomy to the central bank
in formulating and implementing macroeconomic
policies. Thus, there is the need to deepen the
financial sector and reposition it for growth and
integration into the global financial system in
conformity with international best practices.
This study is important at this level of
economic development when efforts are being
made to reposition the financial system to enable
it play key roles in economic development of
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
India. The study essentially seeks to examine in
an empirical manner, the nature of financial
deepening in India since the onset of financial
reforms in 1995up to 2017 when the banking
consolidation took root in India. The study seek
to ascertain the critical factors that have affected
the level of financial deepening in India and to
ascertain if there is observable growth in the
financial deepening index (money supply to
GDP) ratio in India.
The MODEL Specification
A model is identified if it is in a unique
statistical form enabling unique estimates of
the parameters to be subsequently estimated
from a sample data. In this study, the model
used by Gosselin and Parent in their study of
the financial deepening function in pre and
post financial reform periods in India. In
their specifications, six explanatory variables
were used in investigating financial
deepening. In this study, ninevariables were
used.
In
this
model
Financial
Deepening(M/GDP)depends
on,Financial
Savings/GDP ratio (FS/GDP) Private Sector
Credit/GDP (PSC/GDP) value of Cheques
Cleared to GDP ratio (CHQ/GDP), value of
Cheques Cleared to Money Supply (CHQ/M)
the Rate of Inflation (INFLAT), Prime
lending rates(PLR) the intermediation ratio
i.e. Currency outside Banks to Money Supply
(COB/M) and the Dummy.
This model is given as
M/GDPit =f(PLR it ),FS/GDPit,CHQ/GDPit ,C
HQ/MINFLATit ,PSC/GDPit,
DMBA/GDPit,
COB/MS2 + DUM.
Methodology
The data used in this study were
sourced from the Reserve Bank of India
publications and those of the Bureau of
statistics. The data was for the period1995–
2017. The period chosen for the study
encompasses the phases of the major
reforms in the financial system and the
period of consolidation of the banking and
insurance systems in India.
In the present study, financial
deepening defined as the ratio of money
supply to GDP, is a function of the value of
cheques cleared to GDP, value of cheques
to money supply, ratio of private sector
credit to GDP, financial savings to GDP,
rate of inflation, real lending rates, deposit
money bank assets to GDP, Currency
outside Banks to money supply .and the
Dummy.
The equation specified for the study
was estimated using the stepwise least
squares regression method. The model
assists us to determine the T values and
theFvalueswhichwereusedtotestthesignifica
nceoftheequationspecified.
The data used in the regression runs
are as shown in Tables 1.Theseareabsolute
aggregates for each variable obtained for
theperiod1995–2017
(22years).
The
inflation rates are expressed in percentages,
while the savings rates are used as a proxy
for interest rates. These rates are also in
percentages.
The
private
sector
credits(PSC) are aggregate values and so to
financial savings (FS). The introduction of
the dummy variable seeks to capture the
influence of political instability on the
operations of financial institutions and this
to a large extent influences financial
deepening. Values of 0 to 1 are assigned to
the various years: 0 representing mild
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
instability, while 1 represent high levels of
instability. The
data were subsequently
converted to the relevant ratios as shown in
table 1.
To test for stationarity and cointegration, the Durbin – Watson (SBDW)
test was adopted. It is important to note that
the present of co-integration in a model
means
that
long-run
equilibrium
relationship exists among the nonstationery variables.
Results
Regression Results
The summary of financial deepening result
from the Two stage regression analysis is
shown in the model summary below.
Model Summary
R
= 0.972
2
R
= 0.946
2
Adj R
=0.906
Std error of estimate = 0.88808
Durbin-Watson = 1.551
F value
= 23.62
d.f.
= 22
The coefficient of correlation R and
Coefficient of determination R2 measure the
explanatory power of multiple regression
models. From the results, there is a high
coefficient of correlation (97.2percent).
The implication is that the variables in the
equation are useful for explaining the level
of financial deepening that has occurred
between 1995 and 2017. There is also a
highly significant coefficient of determination
(94.6 percent). The standard error of the
estimates also known as residual standard
deviation has a value of 1.77708. The Fstatistic value is found to be 23.62. The F
value is significant at the 5 percent level.
The overall fit of the regression model
measured by the F- statistic, is statistically
significant at this level. The Durbin Watson
(DW) statistic of 1.551 indicates that there is no
problem of serial correlation in the regression
model. This is a case of positive serial
correlation. Also, multicolinearity which often
present in cross-sectional data seems to be non
existent in the model.
In Table 2 the estimation results using
the nine explanatory variables are presented at
alpha equal to 0.05 level of significance and
also at 0.10. It was found that the financial
savings ratio, interest rates, cheques
cleared to GDP ratio and, Deposit Money
Banks Assets to GDP ratio are very useful
explanatory variables. Political instability is
not significant at both the 5 percent and 10
percent levels.
The implication of the findings is that although
the financial structure had enhanced the level
of financial savings and thus affected the level
of financial deepening positively, the financial
system has not been efficient in resource
allocation evidently. Here, the process of
intermediation in the system is not efficiently
done. Although the financial system has not
grown tremendously in size and structure this
has not been translated in the provision of
loans and credits especially to the real
sector of the economy.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Table2:
EstimationResults
Variable
V1 COB/M
V2 RO1
V3 PLA
V4 FS/EDO
Means Std
Dev.
24.97 5.154
T Stat
Remarks
.668
Not Sig
22.82
19.942
.330
Not Sig
20.38
5.502
2.076*
Sig
12.47
7.439
3.453** Sig
V5
57.33 47.764 2.107** Sig
CHQ/GDP
V6 CHQ/MS2 216.53 192.268 (2.492) Not Sig
V7 PSC/GDP 17.30
V8
DMBA 37.07
/EDP
V9 DUM
.27
8.008
(2.232) Not Sig
12.425
6.565** Sig
.456
.606
Not Sig
*Significant at 10 per cent level
**Significant at % per cent level
management in the financial system. Finally
the supervision and regulation of banks
should be strengthened, with a focus on risk
management. These shall be very useful in
enhancing the level of financial deepening in
India.
References:
1.
2.
Conclusion
The study was concluded that the level of
financial deepening in India has remained
relatively low in spite of the various reforms
and institutional changes put in place by the:
monetary authorities. It is also evident that the
low level of monetization of the economy,
the high rate of inflation and the level of
private sector credits have negatively affected
the level of financial deepening in India.
Although the level of interest rates have
remained very high, the level of private sector
credits have not sustained the desired level
of new investments necessary to facilitate
growth in the economy. However, there is an
urgent need to sustain a higher level of
macroeconomic stability in India, reduce the
high incidence of non performing credits
ensure that private sector credits are channeled
to the real sector of the economy, enhance
the level of corporate governance in the
financial system and also strengthen risk
3.
4.
5.
6.
7.
8.
Apergis, Nicholas; Filippidis, Ioannis;
Economidou, Claire (1 April 2007).
"Financial Deepening and Economic
Growth Linkages: A Panel Data
Analysis". Review of World Economics
Raghuram G. Rajan; Luigi Zingales
(2003). "The great reversals: the politics of
financial development in the twentieth
century" (PDF). Journal of Financial
Economics.
(King and Levine, 1993; Levine and
Zervos, 1998)
Raghuram G. Rajan; Luigi Zingales (June
2016). "Financial Dependence and
Growth"(PDF). The American Economic
Review.
"Deepening Rural Financial Markets:
Macroeconomic, Policy and Political
Dimensions"(PDF). Researchgate.net.
Retrieved 5 November 2017.
Mohan, Rakesh (2006-11-03). "Economic
Growth, Financial Deepening and
Financial Inclusion". Retrieved 6
November 2017.
Reserve Bank of India-Various
Publications
www.rbi.ac.in
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
ANALYSIS OF TRENDS AND GROWTH OF DIGITAL RETAIL
PAYMENTS SYSTEM IN INDIA
SUNIL S.
Assistant Professor
Government College, Attingal
Abstract
India with its unique rich payment ecosystem is now emerging as a global one in innovative digital
payment systems. The Reserve Bank of India and the Government have expressed a vision of a less
cash civilization and guided its evolution with feet stick on the ground. The growth of financial
services in India has largely been driven by the banks. The regulator as well as the banks has led the
initial push, development and support of digital payments infrastructure. Non-banks have entered the
market and expanded the range of payment services available to the Indian consumer backed by their
strength in technology and customer oriented innovation. Banks and non-banks are partnering to
offer the combination of trust and innovation to the Indian consumer. This will resulted in a recent
growth in the number of digital payments, should continue.
Keywords: Digital Banking, Digital Payments, Retail Banking,
Introduction
the middle class, the businesses and the nation.
India remains a largely cash based
economy with cash accounting for more than
78% of all retail payments before 2014-15.
Compared to some other countries, like China,
Mexico and Brazil, India ranks very low
relating to Non-cash transactions by non-banks
per capita per annum as well as number of pay
points (for digital payments) per million
people. The cash dependence, in turn, has
impacted government‟s ability to widen tax
compliance
and
increase
tax
Digitisations
of
transactions
revenue.
become
an
obligation for India; it will benefit the poor,
India is significantly behind peers on digital
transactions, and digitization will create a
multiplier effect on efficiency of capital and
resource
allocation
transparency,
through
traceability
of
greater
transactions,
enforce ability of law and significantly buoyed
tax revenues which will make bigger State‟s
resources for social welfare.
Why this study is important?
With a view to encouraging digital
payments and enhancing financial inclusion
through digitalization, the Reserve Bank of
India decided to constitute a High-Level
Committee on Deepening of Digital Payments
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
to assess the existing status of digital payments
of instruction, authorization or order to a bank
and level of digital payments in financial
to debit or credit an account maintained with
inclusion, identify best practices that can be
that bank through electronic means and
adopted, recommend initiatives to strengthen
includes point of sale transfers; automated
safety and security of digital payments, lay
teller machine transactions, direct deposits or
down a plan of action to increase customer
withdrawal of funds, transfers initiated by
confidence in digital financial services, and
telephone, internet and, card payment.
suggest
Digital Payment Systems
a
Medium-Term
strategy
for
deepening of digital payments.
The payment system in India is classified into
two main segments:
Objectives of the Study
1. Instruments which are covered under
1.To study the retail payment systems existing
in India
Systemically
Important
Financial
Market
Infrastructure (SIFMIs), and
2.To analyse the performance evaluation of
growth and trend of digital retail payments in
India
2. Retail Payments.
1. Systemically
Important
Financial
Market Infrastructure (SI-FMI):
Digital Payment Payments Systems in India
Financial Market Infrastructure (FMI): It is
The RBI Ombudsman scheme for
defined as a multilateral system among
digital
transactions
defines
a
„Digital
participating
institutions,
consist
of
the
Transaction‟ as “Digital Transaction‟ means a
operator of the system, used for the purposes
payment transaction in a seamless system
of clearing, settling, or recording payments,
affected without the need for cash at least in
securities, derivatives, or other financial
one of the two legs, if not in both. This
dealings. Under SIFMI, new standards or
includes transactions made through digital /
principles are intended to ensure that the vital
electronic modes wherein both the originator
financial
and the beneficiary use digital / electronic
sustaining global financial markets is even
medium to send or receive money.”
more dynamic and thus even better suited to
market
infrastructure
(FMI)
endure financial shocks than at present.
The Payment and Settlement Act, 2007
has
defined
Digital
Payments,
as
any
Under this segment (SIFMI) there are four
instruments of payments:
"electronic funds transfer" that is any transfer
of funds which is initiated by a person by way
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
RTGS: Real Time Gross Settlement is defined
Forex transactions is done by CCIL which was
as the continuous, real-time settlement of fund
started in 2002.
transfers individually on an order by order
basis without netting. 'Real Time‟ means the
2. Digital Retail Payments:
processing of instructions at the time they are
Under the Retail Payments segment which has
acknowledged rather than at some later time;
a large user base, there are three broad
'Gross Settlement' means the settlement of
categories of instruments. They are Paper
fund transfer instructions occurs individually
Clearing, Retail Electronic Clearing, and Card
on an instruction by instruction basis. This
Payments. The instruments under these three
system is primarily intended to large value
categories are described below:
transactions. The minimum amount to be
Cheque Truncation System (CTS): CTS or
remitted through RTGS is ` 2 lakh. For inter-
online image-based cheque clearing system is
bank fund transfer there is no minimum.
a cheque clearing system undertaken by the
CBLO: Collateralised Borrowing and Lending
Reserve Bank of India (RBI) for faster clearing
Obligation (CBLO) is a money market
of cheques. It eliminates the cost associated
instrument introduced by Clearing Corporation
with the movement of physical cheques.
of India Ltd. (CCIL), in 2003. This represents
Non-MICR: The Non-MICR (Non-Magnetic
an obligation between a borrower and a lender
Ink Character Recognition) clearing refers to
to the terms and conditions of a loan. It also
the process of manual clearing of cheques
does not entail physical transfer of respective
where the cheque is physically moved between
securities from borrower to lender or vice
the bank branches/banks for clearing. MICR is
versa.
a technology used to verify the legitimacy or
Government
Securities:
A
Government
originality of paper documents, especially
Security (G-Sec) is a tradable instrument
checks.
issued by the Central Government or the State
ECS DR/CR: ECS (Electronic Clearing
Governments.
System) is an electronic mode of payment /
Forex Clearing: The term „Forex‟ stands for
receipt for transactions that are repetitive and
Foreign Exchange. In simple terms it is the
periodic in nature. DR/CR is „Debit Record or
trading in currencies from different countries
Credit Record‟. ECS facilitates bulk transfer of
against each other. In India the settlement of
monies from one bank account to many bank
accounts
Government College, Attingal
or
vice
versa.
ECS
includes
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
transactions
processed
under
National
Understanding the importance of mobile
Automated Clearing House (NACH) operated
banking in financial inclusion, *99# was
by National Payments Corporation of India
dedicated to the nation by Hon‟ble Prime
(NPCI).
minister on 28th August 2014, as part of
NEFT: National Electronic Funds Transfer
„Pradhan Manti Jan Dhan Yojna‟.
(NEFT) is a nation-wide payment system
USSD (Unstructured Supplementary Service
facilitating one-to-one funds transfer. Under
Data) is a Global System for Mobile (GSM)
this scheme, individuals, firms and corporate
communication technology that is used to send
can electronically transfer funds from any bank
text
branch to any individual, firm or corporate
application program in the network.
having an account with any other bank branch
NACH: “National Automated Clearing House
in the country participating in the scheme.
(NACH)” is a service offered by NPCI to
IMPS: Immediate Payment Service (IMPS)
banks which aims at facilitating interbank high
offers an instant 24X7 interbank electronic
volume, low value debit/credit transactions,
fund transfer service through mobile phones.
which are repetitive and electronic in nature. It
IMPS are an emphatic tool to transfer money
allows participating banks for centralized
instantly within banks across India through
posting of inward debit/credit transactions and
mobile, internet and ATM. It is offered by
is run by NPCI.
National Payments Corporation of India
Credit Card: A credit card is a card issued by
(NPCI).
a financial company which enables the
UPI: Unified Payments Interface (UPI) is a
cardholder to borrow funds. The issuer pre-sets
system that powers multiple bank accounts
borrowing limits which have a basis on the
into a single mobile application (of any
individual's credit rating. These cards can be
participating bank), merging several banking
used domestically and internationally and can
features, seamless fund routing & merchant
also be used to withdraw cash from an ATM
payments into one hood.
and for transferring funds to bank accounts,
*99#: USSD based mobile banking service of
debit cards and prepaid cards within the
NPCI was initially launched in November
country.
2012. The service had limited reach and only
Debit Cards: A debit card is a payment card
two TSPs (Telecom Service Provider) were
that deducts money directly from a consumer‟s
offering this service i.e. MTNL & BSNL.
bank account to pay for a purchase and
between a
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mobile phone
and
an
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
eliminate the need to carry cash or physical
Banks issuing such PPIs shall also facilitate
checks to make purchases. In addition, they
cash withdrawal at ATMs/Point of Sale
offer the convenience of credit cards for small
(PoS)/Business Correspondents (BCs).
negative balances that might be incurred if the
Analysis and Discussion
account holder has signed up for overdraft
India‟s payment system - particularly,
coverage. However, debit cards usually have
its digital payments system - has been evolving
daily purchase limits.
impotently for the past many years, due to the
Pre-Paid Instruments (PPIs):
PPIs are
developments
in
information
and
payment instruments that facilitate purchase of
communication technology (ICT), and fostered
goods
financial
and in line with the path envisaged by the
services, remittance facilities, etc., against the
Reserve Bank of India. The National Payments
value stored on such instruments. PPIs are
Corporation of India (NPCI) was established
classified under three types:
in 2008 with the aim of achieving the vision by
Closed System PPIs: These PPIs are issued by
the RBI and Government of India. Important
an entity for facilitating the purchase of goods
milestones attained in this overall process of
and services from that entity only and do not
development
permit cash withdrawal.
comprises:
and
services,
including
Semi-closed System PPIs: These PPIs are
used for purchase of goods and services,
including
financial
services,
remittance
a.
of
the
payments
system
The introduction of MICR clearing in
the early 1980s,
b. Electronic
Clearing
Service
and
facilities, etc., at a group of clearly identified
Electronic Funds Transfer in the 1990s,
merchant locations/establishments which have
c. Issuance of credit and debit cards by
a specific contract with the issuer (or contract
through
a
payment
banks in the 1990s,
aggregator/payment
d. The National Financial Switch in 2003
gateway) to accept the PPIs as payment
that brought about interconnectivity of
instruments.
ATMs across the country,
These
instruments
do
not
permit cash withdrawal.
e. The RTGS and NEFT in 2004,
Open System PPIs: These PPIs are issued only
f. The Cheque Truncation System (CTS)
by banks and are used at any merchant for
in 2008,
purchase of goods and services, including
financial services, remittance facilities, etc.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
g. The second factor authentication for
the „card not present‟ transaction in
2009, and
h. The
new
1.1. Overall Growth Performance of digital
retail payments (Volume)
Table: 1.1. Overall Growth Performance of
RTGS
with
enhanced
facilities and features in 2013 [Mundra
digital retail payments (Volume)
Year
(2015)].
Moreover,
non-bank
entities
have
been
2003-04
2004-05
permitted to issue of pre-paid instruments
2005-06
(PPI), including mobile and digital wallets.
2006-07
These have been supported by significant
initiatives of the NPCI including the launching
of
grid-wise
operations
of
CTS,
interoperability on NACH, IMPS, NFS, RuPay
(a domestic card payment network), APBS and
AEPS (which are an important part of the
financial inclusion process), National Unified
USSD.
Here, the growth trends in Digital
Retail Payments over the past years are
discussed. The narrative on the growth trends
2007-08
2008-09
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
Volume
(in Millions)
166.95
228.9
285.03
378.72
535.32
667.81
718.16
908.58
2532.5
2939.5
3627.7
4620.9
6945.2
10879.7
15760.6
Growth in
Volume (%)
0
37.11
24.52
32.87
41.35
24.75
7.54
26.51
178.73
16.07
23.41
27.38
50.3
56.65
44.86
Source: Reserve Bank of India (2019)
Graph: 1.1. Trend in Retail Digital
Payments – Volume
which covers the period from 2003-04 to
2017-18 is presented. The analysis covers the
trends over the years 2003-04 to 2015-16 ie.,
the
years
preceding
demonetization and
compares the growth trends over the last two
years ie. 2016-17 and 2017-18 this is the post
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
200
150
100
50
0
demonetization period. The analysis of trend
and growth of digital retail payment is made
on the basis of data provided by reserve Bank
of India during the respective periods.
1.Overall Growth Trend of Retail Payment:
Government College, Attingal
Volume (in Millions)
Growth in Volume (%)
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
The values of volume of overall digital retail
2016-17
220634
24.12
payment are measured in the primary axis and
2017-18
285612
29.45
Source: Reserve Bank of India (2019)
growth of volume on the secondary axis. From
the above table and figure the volume of
overall retail payments steadily increased over
the period from 2011-12 to 2017-18, recording
a compound average annual growth rate
(CAGR) of over 39.47 per cent. The rate of
growth in volume of overall retail payments
further accelerated to 44.86% per cent in 2017-
Graph: 1.2. Trend of Overall Growth
Performance of Retail Payments – Value
300000
900
800
700
600
500
400
300
200
100
0
-100
250000
200000
150000
100000
18. Graph 1 indicates the trends in Retail
50000
Digital Payments over the period of 2003-04 to
0
2017-18. The growth in 2011-12 is spectacular
and could be attributed to development of
innovative digital payments platform. But
during 2017 -18 the growth rate is declining
Value (in Billion)
Growth in Value (%)
Source: Reserve Bank of India (2019)
from 56.65% (2016-17) to 44.86% (2017-18).
The values of value of overall digital retail
1.1. Overall Growth Performance of Retail
payment are measured in the primary axis and
growth of value on the secondary axis. The
Payments (Value):
Table: 1.2. Overall Growth Performance of
above figure shows that the nominal value of
Retail Payments (Value)
retail payments has a cyclical movement over
2003-04 to 2014-15, though it has a steady
Year
2003-04
2004-05
2005-06
2006-07
Value (in Billion)
521.44
1087.49
1463.81
2356.93
Growth in Value( %)
0
108.56
34.6
61.01
2007-08
10419.91
342.1
2008-09
2009-10
5003.22
-51.98
6848.86
36.89
But the annual growth has increased to 29.45%
2010-11
13086.88
91.08
in 2017-18 due to demonetization.
2011-12
121149.9
825.74
2012-13
134114.4
10.7
2013-14
143447.4
6.96
2014-15
154129.3
7.45
2015-16
177753
15.33
growth of 825.74% in the year 2011-12, and
a decline in the growth from 2012-13 to 201415. Thereafter, an increase in value of retail
payments records a CAGR of 102.8 per cent.
2. Instrument Wise Growth Trends of
Retail Payments:
2.1. ECS Debit (Volume and Value):
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
The following table (table 2.1) and graph
(graph 2.1) shows the trend of both volume
and value of ECS Debit transactions during the
Graph: 2.1. ECS Debit (Volume and Value)
period from 2003-04 to 2017-18.
250
2000
1800
1600
1400
1200
1000
800
600
400
200
0
From the Graph 2.1 below, the values of
volume of ECS Debit is measured in the
200
primary axis and value of ECS Debit on the
150
secondary axis. It is observed that both the
100
volume and value of ECS Debit increases from
50
2003-04 to 2008-09 at an increasing rate. From
2009-10 onwards up to 2014-15 there is a
0
linear rate of increase. During the period 201415 and 2015-16 it get stabilizes and start
Volume (in…
declining at a higher rate during 2016-17 and
Source: Reserve Bank of India (2019)
2017-18.
1.1. ECS Credit (Volume and Value)
Table: 2.1. ECS Debit (Volume and Value)
Table: 2.2. ECS Credit (Volume and Value)
Year
Volume
(in Millions)
Value
(in Billions)
Year
Volume
(in Millions)
Value
(in Billions)
2003-04
20.32
102.28
2003-04
7.87
22.54
2004-05
40.05
201.80
2004-05
15.30
29.21
2005-06
44.22
323.24
2005-06
35.96
129.86
2006-07
69.02
832.73
2006-07
75.20
254.41
2007-08
78.37
7822.22
2007-08
127.12
489.37
2008-09
88.39
974.87
2008-09
160.05
669.76
98.13
1176.13
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
149.28
695.24
117.30
1816.86
156.74
736.46
164.70
176.50
192.90
226.00
224.80
8.80
1.50
833.60
1083.10
1268.00
1739.80
1652.00
39.00
10.00
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
121.50
122.20
152.50
115.30
39.00
10.10
6.10
1837.80
1771.30
2492.20
2019.10
1059.00
144.00
115.00
Source: Reserve Bank of India (2019)
Source: Reserve Bank of India (2019)
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Graph: 2.2. Trend in ECS Credit Volume
and Value
180
160
140
120
100
80
60
40
20
0
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
132.34
9391.49
226.10
394.10
661.00
927.60
1252.90
1622.10
1946.40
17903.50
29022.40
43785.50
59803.80
83273.00
120040.00
172229.00
Source: Reserve Bank of India (2019)
Graph: 2.3. EFT/NEFT volume and Value
2500
200000
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
2000
1500
Volume (in Millions)
Value (in Billions)
1000
500
Source: Reserve Bank of India (2019)
The value of volume of ECS Credit is measured
0
in the primary axis and value of ECS Credit on
the secondary axis.
From the above, both
volume and value of ECS credit increase from
2003-04 to 2013-14 with an exceptional increase
in the value of ECS credit during the year 200708. From 2014-15 to 2017-18 it shows that both
the volume and value declining at an increasing
Volume (in Millions)
Value (in Billions)
Source: Reserve Bank of India (2019)
The following table and graph describe the
trend and growth of volume and value of
EFT/NEFT for the period from 2003-04 to
2017-18. Both volume and value move on the
rate.
1.1.EFT/NEFT volume and Value:
Table: 2.3. EFT/NEFT volume and Value
same direction at an increasing rate especially
after 2009-10 up to 2017-18. The value of
volume of EFT/NEFT is measured in the
Year
Volume
(in Millions)
Value
(in Billions)
2003-04
0.82
171.25
2004-05
2.55
546.01
2005-06
3.07
612.88
2006-07
4.78
774.46
from 2003-04 to 2009-10 shows stagnation
2007-08
13.32
1403.26
and afterwards an increase in both up to 2014-
2008-09
32.16
2519.56
2009-10
66.34
4095.07
primary axis and value of EFT/NEFT on the
secondary axis.
From the chart and graph
below, the volume and value of EFT/NEFT
15. The rate of growth increases from 2015-16
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
to 2017-18, due to increased penetration of
digital payments and demonetisation.
1.1. Credit (CR) Card
Graph: 2.4. Credit (CR) Card
The value of volume of credit card transactions
1600
is measured in the primary axis and value of
1400
credit card transactions on the secondary axis.
1200
From the following graph, the volume and
value of credit card transactions shows a
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
1000
800
600
constant growth from 2003-04 to 2008-09.
400
During the year the there is a negative growth
200
in both volume and value on 2009-10. From
0
2010-11 onwards the rate of growth in credit
cards declining up to 2014-15 and thereafter an
increase in growth rate of credit card
transactions from 2015-16 to 2017-18.
Volume (in Millions)
Value (in Billions)
Source: Reserve Bank of India (2019)
1.1.Debit (DR) Card
Table: 2.4. Credit (CR) Card
Table: 2.5. Debit (DR) Card
Year
Year
Volume
(in Millions)
Value
(in Billions)
2003-04
100.18
176.63
2004-05
129.47
256.86
2005-06
156.09
338.86
2006-07
169.54
413.61
2007-08
228.20
579.85
2008-09
259.56
653.56
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
234.24
618.24
265.14
755.16
320.00
396.60
509.10
615.10
785.70
1087.10
1405.20
966.10
1229.50
1539.90
1899.20
2407.00
3284.00
4590.00
Volume
(in Millions)
Value
(in Billions)
2003-04
37.76
48.74
2004-05
41.53
53.61
2005-06
45.69
58.97
2006-07
60.18
81.72
2007-08
88.31
125.21
2008-09
127.65
185.47
2009-10
2010-11
2011-12
2012-13
2013-14
2014-15
2015-16
2016-17
2017-18
170.17
264.18
237.06
386.91
327.50
469.10
619.10
808.10
1173.50
2399.30
3343.40
534.30
743.40
954.10
1213.40
1589.00
3299.00
4601.00
Source: Reserve Bank of India (2019)
Source: Reserve Bank of India (2019)
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
2017-18, recording the rate of growth in
volume of overall retail payments further
Graph: 2.5. Debit (DR) Card
4000
3500
3000
2500
2000
1500
1000
500
0
5000
4000
accelerated to 44.86% per cent in 2017-18.
c. The growth in 2011-12 is spectacular and
3000
could be attributed to development of
2000
innovative digital payments platform. But
1000
during 2017 -18 the growth rate is
0
declining from 56.65% (2016-17) to
44.86% (2017-18).
2. Value of Overall Digital Retail Payments
Volume (in Millions)
Value (in Billions)
a. The value of retail payments has a cyclical
movement over 2003-04 to 2014-15,
Source: Reserve Bank of India (2019)
The value of volume of debit card transactions
though it has a steady growth of 825.74%
is measured in the primary axis and value of
in the year 2011-12,
debit card transactions on the secondary axis.
Graph 2.5 above states that the growth of
b. There was a decline in the growth of value
from 2012-13 to 2014-15.
volume and value of debit card transactions
c. An increase in value of retail payments
increases from 2003-04 to 2009-10, but the
records a CAGR of 102.8 per cent between
pace of growth is less. From 2011-12 to 2014-
2003-04 to 2017-18, but the annual growth
15 the growth of debit card transactions in
has increased to 29.45% in 2017-18 due to
terms of volume and value decreases and then
demonetization.
shows a steep increase in both in 2016-17 and
3. It is observed that both the volume and
it shows a slow pace during 2017-18.
value of ECS Debit increases from 2003-
Results
04 to 2008-09 at an increasing rate. From
1. Volume
of
Overall
Digital
Retail
linear rate of increase. During the period
payments
a. In India, compound average annual growth
rate
(CAGR)
of
total
digital
retail
payments (in volume) is 39.47 per cent.
b.
2009-10 onwards up to 2014-15 there is a
2014-15 and 2015-16 it get stabilizes and
start declining at a higher rate during 201617 and 2017-18.
Volume of overall retail payments steadily
4. Both volume and value of ECS credit
increased over the period from 2011-12 to
increase from 2003-04 to 2013-14 with an
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
exceptional increase in the value of ECS
credit during the year 2007-08. From 201415 to 2017-18 it shows that both the
volume
and
value
declining
at
an
increasing rate.
5. The volume and value of EFT/NEFT from
2003-04 to 2009-10 shows stagnation and
afterwards an increase in both up to 201415. The rate of growth increases from
2015-16 to 2017-18, due to increased
penetration
of
digital
payments
and
demonetisation.
6. The volume and value of credit card
transactions shows a constant growth from
2003-04 to 2008-09. During the year the
there is a negative growth in both volume
and value on 2009-10. From 2010-11
onwards the rate of growth in credit cards
declining up to 2014-15 and thereafter an
increase in growth rate of credit card
transactions from 2015-16 to 2017-18.
7. The growth of volume and value of debit
card transactions increases from 2003-04
to 2009-10, but the pace of growth is less.
From 2011-12 to 2014-15 the growth of
debit card transactions in terms of volume
Reference:
1. Digital payment: Trends, Issues, and
Opportunities, NITI Aayog, July, 2018
2. Aarti Sharma and Nidhi Piplani,
2017, International Research Journal
of Management Science & Technology,
IRJMST Vol 8 Issue 1 [Year 2017] ISSN
2250 – 1959, http://www.irjmst.com
3. RBI Report on Trend And Progress of
Banking In India 2009-10, 2010-11,
2011-12, 2012-13, 2013-14, 2014-15,
2015-16, 2016-17, 2017-18.
4. RBI Monthly Bulletin March 2013,
2014,2015, 2016,2017, & 2018
5. CURRENT STATISTICS: Money &
Banking, No. 9A: Retail Electronic
Payment Systems, RBI Monthly
Bulletin, November, 2012
6. BCG Google Digital Payments 2020:
The Making of a $500 Billion
ecosystem in India, July, 2016_tcm2139245 http://image-src.bcg.com/IndianBanking-2020-Sep-2010_tcm2128897.
7. NITI Aayog, Government of India,
Interim Report of the Committee of
Chief Ministers on #Digital Payments,
January, 2017
8. Rajasekhara v Maiya, 2017 6
Technology Trends That Will
Transform Banking In 2017. (2017,
January 2). Retrieved from
Huffington Post India website:
https://www.huffingtonpost.in/rajas
hekara-v-maiya/6-technologytrends-that-will-transform-bankingin-2017_a_21645614/
and value decreases and then shows a steep
increase in both in 2016-17 and it shows a
slow pace during 2017-18.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
KILLING THE GOLDEN GOOSE- THE CASE OF PRIVATE BUSES
IN KERALA
PRAGEETH.P
Research Scholar
Government College, Attingal
Dr. ANZER R.N
Assistant Professor
Government College, Nedumangad
ABSTRACT
Public Transportation services are integral to societies and are vital for civic life. Recently,
many countries have twisted their attention towards emerging and refining their public transport
system. But, here in our country especially in the state of Kerala, the government is doing all the
obligatory procedures towards Public transport system (KSRTC) and Private transport services.
Frequently changing laws and implementing strict rules which are against the industry. This study
tries to find out the problems been faced by the Private bus sector in the State of Kerala. This study is
an innovative one and will help to understand the reason for the downfall of the industry during the
recent times.
Key Words: Public Transportation, Private Bus
Introduction:
population, and hence the growth of the sector
Transportation has been the key to
mobility.
Transportation
helps
to
move
in India requires major requirements.
Public
Transportation services
are
peoples and goods from one place to another.
integral to societies. Countries need effective
Realising
public
public transport services for transit users, who
transport, countries around the world have
need and value different modes of public
been investing huge amounts of money into
transport. Public Transportation is defined as
the
transportation by means of conveyance that
the
huge
transportation
potential
sector
of
every
year.
Transportation is essentially a derived demand
provides
continuing
depending upon the size and structure of the
transportation to the public, which includes
economy and the demographic profile of the
school buses, Charter and sightseeing services
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general
or
specific
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
which includes various modes such as buses,
Private Bus Operation in Kerala- A
subways, rails, trolleys and `ferry boats [ Train
Present Scenario
and Weiner]. In an urbanized society, an
Private –Operated buses are efficient
efficient transportation system is one of the
and add value to government‟s exchequer
basic components of the social, economic and
saving it from the liability of providing
physical structure.
transport. The Government is earning high
Development of public transportation
system is costly. Thus, private investment is
often critical and considered effective in
delivering the required products and services.
For instance, incentives and competition have
through taxes on private-operated buses, with
estimated earnings of ₹ 120000 from each
private bus annually by way of road tax. The
government earns around ₹750 crores from
private bus operators every year.
enabled private players to provide highly
Nowadays, private bus operations are
efficient transport systems. Private sector
in a state of roadblock. Government are
involvement in building and facilitating public
imposing and changing laws regularly, which
transportation has generated positive outcome
affects the proper functioning of this sector.
around the world.
Earlier private buses were seen as a dignity of
Kerala is one of the highly urbanised
states in India (47.72 per cent as per census
power, but now this sector is finding difficult
to earn its working capital.
2011) and has a significant number of people
As the government is focusing to bring
covering long distances between 20 and 300
KSRTC in the way of making profit, more
kilometres. Cities in Kerala rank high in Public
restriction isbeing imposed on the opponents.
Transport Accessibility Index and City Bus
Renewal of permit are not being made,
Transport
high
takeover of route permit by KSRTC, heavy
penetration of public transportation buses. The
road tax and Insurance etc. made this sector in
composition of public bus system is one of the
the state of diminishing. Many owners are
highest in the country. Kerala is unique for its
surrendering their permit to the RTO because
high public transport model share, Thanks to
on the heavy loss in this sector. As per reports,
the role played by private-operated buses. The
non-renewal or shutting down of private bus
major share being held by private sector but
sector would affect five to six employees of
now being captured by KSRTC.
private bus adversely.
Supply
Index
with
a
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Assessing, rural to urban trips, we can
The hike in fuel price, increase in tax
see that people rely more on private operated
and insurance hasbrought in various challenges
bus. However, now KSRTC took over major
for the private bus sector. Drastic changes
permits and after few operations, they are
were proposed by private bus owners to
stopping their services, now people are finding
overcome this challenge. Government had
difficult for their journey. Increase in use of
responded to the demands of the bus industry
two-wheelers, private owned cars paved the
and agreed for a fare hike. The implications of
way for collapse of this private bus sector.
the above mentioned changes are to be
Driving private bus operations out of
the market will lead thousands jobless. A huge
part of income from private bus goes to lowincome
and
self-employed
populations.
Government policies not only disrupt a normal
and healthy market mechanism but also kill the
analysed in depth. Therefore, the present study
is entitled as “Killing the Golden Goose- the
Case of Private Buses in Kerala”
Limitation of the Study:
The study is limited to only 14 private buses in
the state
incentive for private and public operators to
Only buses from Kollam district were taken as
provide better service to passengers
part of this study
Objectives of the Study:
Analysis and Interpretation
1.To study the performance Evaluation of
1.
private buses
Evaluation of private buses in Kerala
2.To understand the trend in
differentexpenditures related
Comparison of Receipt and Payment of 14 Private
Buses
Collection of
Total
Profit
Research Methodology:
Primary
data
was
To study the performance
essential
to
Year
The Year
Expenses
understand the performance of private buses.
2008-09
2,51,59,576
2,27,73,294
23,86,282
The Primary information was collected from
2009-10
2,44,31,584
2,20,58,246
23,73,338
2010-11
2,58,40,422
2,37,25,331
21,15,091
2011-12
2,82,50,601
2,58,61,492
23,89,109
2012-13
2,99,65,419
2,76,37,177
23,28,242
trip sheet of various private bus operations.
Research Problem:
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
11 years. From the table it is clear that the
2013-14
2,86,00,144
2,72,83,723
13,16,421
2014-15
3,00,99,594
2,77,20,652
23,78,942
2015-16
3,26,30,614
3,00,43,151
25,87,463
2016-17
3,16,29,985
2,99,54,633
16,75,352
2017-18
3,18,66,460
3,12,83,367
5,83,093
rate. This resulted in decrease of profit during
2018-19
3,27,74,294
3,22,81,533
4,92,761
the recent years from 2016 to 2019. To analyse
collection
is
increasing,
the
collection
increased due to the increase in the minimum
fare charge from year to year. At the same time
the total expenses are increasing at a higher
Source: Compiled from Bus owners
the trend in these three variables graphical
The above table shows the collection, total
presentation by fitting trend line is used. The
expense and the profit of 14 private buses over
results are given below.
Receipt and Payment
40,000,000
35,000,000
y = 85523x + 2E+07
R² = 0.888
30,000,000
y = 1E+06x + 2E+07
R² = 0.956
25,000,000
20,000,000
15,000,000
10,000,000
y = -15909x + 3E+06
R² = 0.485
5,000,000
0
2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19
Collection
Total Expenditure
Government College, Attingal
Profit
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
From the line chart for the time series
Rs.85523
every
year.
Likely,
for
total
of collection, total expenses and profit of
expenditures also, an annual linear increase of
selected private transport companies during the
Rs1000000 is seen with an explanation of 95.6
last 11 years, it can be seen that both collection
per cent (R2 =95.6). Thus it can be observed
and total expenses are increasing over the
that total expenditures are increasing at a
years. But a slight decrease in trend is seen in
higher rate than the total receipts i.e.
the profit. An observation of linear regression
Collections. As a result of this, the profit
line fitted to all data gives a detailed
seems to have an annual decrease of Rs15909
explanation of rate of change over time in all
with an explanation of 48.5 per cent (R2
variables. Thus of collections, an annual linear
=48.5). Thus the profit per year is decreasing
increase of Rs.85523 is observed with an
at the rate of Rs.15909 every year.
explanation of 88.8 per cent (R2 =88.8). That is
collections are increasing at the rate of
2. To understand the trend in different
expenditures related
Various Expenditure Table
Year
Accident
Diesel and Oil
Damage
Insurance
Repairs and
Wages and
and Tax
Maintenance
allowances
2008-09
26,528
1,34,70,033
15,60,186
29,56,665
47,59,882
2009-10
97,677
1,27,62,053
15,85,486
28,71,151
47,41,879
2010-11
35,677
1,35,85,084
16,84,060
29,54,793
54,65,717
2011-12
44,557
1,38,57,964
16,95,378
37,41,739
65,21,854
2012-13
38,495
1,45,14,292
20,80,382
37,13,337
72,90,671
2013-14
25,351
1,48,39,427
19,77,974
31,00,252
73,40,719
2014-15
24,341
1,50,29,056
20,12,376
30,98,689
75,56,190
2015-16
34,221
1,52,28,576
20,86,661
33,55,268
93,38,425
2016-17
10,199
1,59,12,819
18,38,745
25,79,373
96,13,497
2017-18
27,598
1,65,32,926
20,17,572
29,43,823
97,61,448
2018-19
3,740
1,84,86,763
19,76,757
29,77,682
88,36,591
Source : Compiled data from bus owners
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
The
above
table
indicates
the
various
table that expenses under each head during the
expenditure incurred by private bus during the
past
few
years
are
increasing.
course of its operations. It is clear from the
Various Expenditure
20,000,000
18,000,000
16,000,000
y = 45823x + 1E+07
R² = 0.879
accident damage
Axis Title
14,000,000
Diesel and Oil
12,000,000
10,000,000
8,000,000
y = -19255x + 3E+06
R² = 0.032
4,000,000
0
Repairs and maintenance
Wages and Allowances
6,000,000
2,000,000
Insurance and Tax
y = 53458x + 4E+06
R² = 0.901
y = -4595.6x + 61063
R² = 0.3915
y = 45362x + 2E+06
R² = 0.5673
Axis Title
From the line chart for the time series
regression line fitted to all data gives a detailed
of various expenditure of selected private
explanation of rate of change over time in all
transport companies during the last 11 years, it
variables. Thus for Diesel and oil expenditure,
can be seen that the diesel and oil expenses are
an annual linear increase of Rs 458239 is
increasing over the years and a slight increase
observed with an explanation of 87.97per cent
in Insurance and Tax expense can also be seen.
(R2 =87.97). That is collections are increasing
But a decrease in trend is seen in other
at the rate of Rs.458239 every year. Likely, for
expenses such as repairs and maintenance.,
Wages and Allowances also, an annual linear
wages and allowances can also have been seen.
increase of Rs 534580 is seen with an
However, accident expense shows a steady
explanation of 90.12 per cent (R2 =90.12).
trend over the years. An observation of linear
Similarly, for Repairs and Maintenance also,
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
an annual linear decrease of Rs 19255is seen
 During 2018-19 (Rs. 88,36,591) wages
with an explanation of 3.12 per cent (R2
and
=3.12). Likely,for Insurance and Tax also, an
comparing 2017-18 (Rs. 97,61,448).
annual linear increase of Rs 445362 is seen
This decrease occurred as the owners
with an explanation of 56.73 per cent (R2
were forced to abridged down the
=56.7). Like Wise, for Accident Damage also,
number of employees
allowances
has
decreased
an annual linear decrease of Rs 4595.6is seen
with an explanation of 39.15 per cent (R2
=39.15).
Suggestions
 To overcome the decline in profit the
Findings
government should rise the minimum
bus fare and should increase the
The major findings drawn from the
concession rate
study are as follows
 Reducing tax rates and insurance
 Profits of the private bus industry
during the past eleven years i.e. from
amount by the government will be a lift
to the economy
[2008-09 to 2018-19] is decreasing
 Instead of collecting tax in quarter the
over the years i.e. in 2008-09 profit
government should collect taxes half
was Rs 23,86,282 and in 2018-19 it
yearly
came
down
to
Rs
4,92,761,
a
diminution of Rs 18,93,521 occurred
 Subsidies
for
fuel
for
public
transportation should be made
 Strict laws should be framed by the
during the years
 While analysing the per unit profit of
government
to
use
public
fourteen buses over the past eleven
transportation for e.g.: as made in
years four buses are facing losses
Delhi Single and Double number
during the last three years
permit should be implemented. If such
 Increase in various expenditure related
laws are made this will boost the
with the industry such as diesel and oil,
industry and also helps in reduces
Spare parts, Road Tax and Insurance
pollution.
over the past few years are the major
reason for the doleful condition of the
industry
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
2. Kadam B. S. (2012): “Work Life
Conclusion:
The private bus industry in the state is
in the doldrums with the number of the buses
on the road coming down from 34000 in 2011
to 12500 by September 2018. Earlier, in the
state of Kerala, private buses were seen as a
symbol of status. This industry provides more
revenue to the government directly and
indirectly and also creates more employment
opportunities. But now this industry has
become a headache to the owners and some are
still
continuing
services
as
part
of
commitment. In September 25 2018 RTA
Kollam has faced surrendering of 14 route
permit in a single day. If this scenario
continues Kerala economy as well as the state
will be facing many problems from this sector.
Therefore, steps should be taken by the
government
and the authorities to
find
solutions in this sector.
Balance: Dilemma of Modern SocietyA Special Reference to Women Bus
Conductors
in
International
MSRTC”,
Journal
of
Zenith
Business
Economics & Management Research,
Vol.2, Issue 2, Feb. 2012.
3. V. Vijay and Durga Prasad (2011):
“Passenger
Pradesh
Amenities
State
Road
of
Andhra
Transport
Corporation (APSRTC)” Asian Journal
of Business Management Studies 2(2),
Pp76-83.
4. Mane K. H. (2010): “Commuters
Satisfaction
with
ServiceProvide
International
Reference
by
Referred
to
MSRTC”,
Research
Journal,Vol. II, Issue 18, July, 2010.
5. Bishnoi N. K. Sujarat (2010), “An
Analysis
of
Profitability
andProductivity of Haryana State Road
Transport
References:
1. Gawali
S.N.
and
Waghere
Y.M.
(2013): “Life Line of Maharashtra –
Maharashtra State Road Transport
Corporation
(MSRTC)”
Online
International Interdisciplinary Research
Journal,
9598,
(Bi-Monthly),
Volume-III,Issue-II,
ISSN2249-
Undertaking(HSRTU)”,
ENVISION – Apeejay‟s Commerce
and ManagementJournal, Pp. 32-41
6. Jadav Chandra and Amar (2010),
“Towards
Competitions”,Indian
Sustainable
Economic
Review (Special Number) Vol.18.
Mar-Apr
2013
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
TRENDS AND GROWTH OF TOURISM IN KERALA
THANSIYA N
Research scholar (full time)
Government college Attingal
Abstract
Tourism is a globally accepted industry because of its economic- social and cultural contributions.
Kerala, the green gateway of India, has today found a niche for herself in the international tourism
map, from the point of view of tourist inflow as well as investments in tourism related sectors. In the
state of Kerala, both the domestic and foreign tourist arrivals are increasing day by day. There is an
influence of changing season of Kerala towards the number of arrivals of tourists. Tourism industry
of Kerala is an indicator of economic growth in terms of foreign exchange earnings; employment and
infrastructure. Here, an attempt is made to analyze the trends and growth in this sector.
Introduction
Tourism is considered as one of the
tourists from all over the world, especially
driving elements to the progress of economy.
from the UK, USA, France and Australia.
Tourism is a globally accepted industry
Kerala Tourism is to position itself as a global
because of its economic- social and cultural
destination for tourism, based on the advantage
contributions. Tourism contributes towards
of the local resources, thereby attracting
complete growth and development of a
investment
country: one, by bringing numerous economic
development for the people of Kerala. An
value & benefits; and, second, helping in build
equable climate, a long shoreline with serene
country's brand value, image & identity.
beaches,
Tourism industry goes beyond attractive
backwaters, lush green hill stations and exotic
destinations, to being an important economic
wildlife, waterfalls, sprawling plantations and
growth contributor. Kerala has a noticeable
paddy fields, Ayurvedic health holidays,
role in the world tourism map and the
enchanting
opportunities
historic and cultural monuments, and exotic
are
opened
wider.
Kerala
Tourism is having a global presence and with
and
resulting
tranquil
art
stretches
forms,
in
sustainable
of
magical
emerald
festivals,
cuisines, make Kerala a unique experience.
its clear strategy for growth and sheer
marketing activities, it has gained a lot of
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Objective
Results and discussions
To understand the trends and growth of
Table 1.1 TOURIST ARRIVALS 2007 –
2018 Tourist arrival in Kerala for the last
12 years
tourism industry of Kerala for the last ten
years.
Methodology
Yea
r
No. of
Domestic
Tourist
Visits
%
Incre
ase
No. of
Foreign
Tourist
Visits
%
Incre
ase
Total no.
of
tourists
%
Incre
ase
The data is collected from the annual
2007
2008
6642941
7591250
5.92
14.28
515808
598929
20.37
16.11
7158749
8190179
6.84
14.41
publication of Department of Tourism, Kerala.
2009
7913537
4.25
557258
-6.96
8470795
3.43
2010
8595075
8.61
659265
18.31
9254340
9.25
2011
9381455
9.15
732985
11.18
10114440
9.29
2012
10076854
7.41
793696
8.28
10870550
7.48
2013
10857811
7.75
858143
8.12
11715954
7.78
2014
11695411
7.71
923366
7.6
12618777
7.71
2015
12465571
6.59
977479
5.86
13443050
6.53
2016
13172535
5.67
1038419
6.23
14210954
5.71
2017
14673520
11.39
1091870
5.15
15765390
10.94
2018
15604661
6.35
1096407
0.42
16701068
5.94
The study is based on secondary data.
Source tourism statistics 2018, Department of Tourism, Gvt of Kerala
Figure 1.1 TOURIST ARRIVALS 2007 – 2018 Tourist arrival in Kerala for the last 12 years
Toursist Arrivals in Kerala (2007-2018)
18000000
y = 84834x - 2E+09
R² = 0.989
16000000
14000000
Axis Title
12000000
y = 79093x - 2E+09
R² = 0.988
10000000
No. of Domestic Tourist Visits
8000000
No. of Foreign Tourist Visits
6000000
Total no. of tourists
y = 57405x - 1E+08
R² = 0.982
4000000
2000000
0
2006
2008
2010
2012
2014
2016
Axis Title
Government College, Attingal
2018
2020
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
It is clear from the table; the number of
domestic tourist arrivals in Kerala in 2007 is
6642941 which show 5.92 per cent of increase
from that of 2006. Number of foreign tourist
arrivals in 2007 shows 20.37 percent increase
from that of 2006. In 2008 there is a notable
increase in tourist arrivals of both domestic
and foreign 14.28 per cent and 16.11 per cent
respectively. Domestic tourist arrivals are
greater than the number of foreign tourists
visit. It is evident from the table that each year
shows an increase in the number of domestic
and foreign tourists visiting the state of Kerala.
In 2017, domestic tourist arrival is 11.39 per
cent and the foreign tourist arrivals are 5.15
per cent more than the year 2016. For the latest
year 2018, domestic tourist arrivals are 6.35
per cent and .42 per cent increase in foreign
tourist arrivals compared to 2017.
The total number of tourist arrivals increased
from 7158749 in 2007 to 16701068 in 2018. It
seems to be a linear trend with an annual linear
growth of 84834 tourists. The R2 value is .989
which gives a good explanation to the model.
The number of foreign tourist arrivals
increased from 515808 in 2007 to 1096407
in 2018. It seems to be a linear trend with an
annual linear growth of 57405 tourists. The
R2 value is .982 which gives a good
explanation to the model. The number of
domestic tourist arrivals increased from
6642941 in 2007 to 15604661 in 2018. It
seems to be a linear trend with an annual linear
growth of 79093 tourists. The R2 value is .988
which gives a good explanation to the model.
TABLE 1.2 - FOREIGN TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017
& 2018
FOREIGN
I – Quarter
2014
340193
2015
363492
2016
384719
2017
393038
2018
440694
II – Quarter
142641
151774
153461
175746
167666
III – Quarter
172731
184005
200335
200988
173758
IV – Quarter
267801
278208
299904
322098
314289
923366
977479
1038419
Total
Source tourism statistics 2018, Department of Tourism, Gvt of Kerala
1091870
109
Foreign tourist arrivals generate foreign
exchange earnings of India.Kerala Tourism
aiming to change Kerala into a 365 days
tourist destination. During 2018, the maximum
number of foreign tourists arrived in January
followed by February. The maximum number
of foreign tourists arrived during the 1st
quarter of the year 2018, constituting 40.19%
with 440694 tourists, followed by 4th quarter
constituting 28.67% with 314289 tourists, the
3rd quarter constituting 15.85% with 173758
tourists, and the 2nd quarter constituting
15.29% with 167666 tourists.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Figure- 1.2 FOREIGN TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 &
2018
1200000
1000000
800000
Series1
600000
Series2
400000
Series3
Series4
200000
0
FOREIGN
I – Quarter
II – Quarter
III – Quarter IV – Quarter
Total
Table 1.3 DOMESTIC TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 &
2018
DOMESTIC
I – Quarter
2014
2685048
2015
2878897
2016
3043809
2017
3270514
2018
3877712
II – Quarter
2776042
2976682
3110808
3578943
4149122
III – Quarter
2647557
2861813
3086508
3410654
3292016
IV – Quarter
3586764
3748179
3931410
4413409
4285811
Total
11695411
12465571
13172535
14673520
15604661
Source tourism statistics 2018, Department of Tourism, Gvt of Kerala
Figure 1.3 DOMESTIC TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 &
2018
Total
IV – Quarter
Series5
III – Quarter
Series4
II – Quarter
Series3
I – Quarter
Series2
DOMESTIC
Series1
0
5000000
10000000
15000000
Government College, Attingal
20000000
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Figure 1.4 FOREIGN EXCHANGE
EARNINGS FROM TOURISM FOR LAST
12 YEARS (` In Crores)
Foreign Exchange Earnings from
Tourists from 2007 to 2018
10000
9000
8000
y = 604.3x + 1485.
R² = 0.978
7000
Axis Title
From the table it is clear that during the year
2014, domestic tourist arrival is higher in the
4th quarter. In 2015, 2016, 2017 and 2018
show the same trend of increase for the 4th
quarter which consists of October November
and December.
For the year 2014 domestic tourist arrival is
lower in the third quarter; in 2015 also the
minimum is in the third quarter; for the year
2016 and 2017 it is in the first quarter and for
the year 2018 lowest domestic tourist arrival is
in the third quarter.
During 2018, the maximum number of
domestic tourists arrived during the 4th quarter
constituting 27.46% with 4285811 tourists
followed by 2nd quarter constituting 26.59 %
with 4149122 tourists, the 1st quarter
constituting 24.85% with 3877712 tourists and
the 3rd quarter constituting 21.10% with
3292016 tourists.
6000
5000
4000
3000
2000
1000
0
0
5
10
15
Axis Title
Table 1.4 FOREIGN EXCHANGE
EARNINGS FROM TOURISM FOR LAST
12 YEARS (` In Crores)
Year Earnings % of variation over
previous year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2640.94
3066.52
2853.16
3797.37
4221.99
4571.69
5560.77
6398.93
6949.88
7749.51
8392.11
8764.46
32.82
16.11
-6.96
33.09
11.18
8.28
21.63
15.07
8.61
11.51
8.29
4.44
Source tourism statistics 2018, Department of Tourism, Gvt of Kerala
From the graphical inference, it is clear that
the amount of foreign exchange earnings
increases from 2640.94 crore in 2007 to
8764.46 crore in 2018. It seems to be a linear
trend with an annual linear growth of 604.3
crore. The R2 value is .978 which gives a good
explanation to the model.
Foreign exchange earnings from tourism have
shown a steady growth over the years. In 2018,
Kerala has earned ` 8764.46 crores as foreign
exchange earnings from tourism against `
8392.11 crores in the year 2017 showing a
growth of 4.44%. Table 3.7 and Graph 3.7
shows the estimates of earnings from foreign
tourists in the last ten years.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Table 1.5 MONTH WISE ARRIVAL DETAILS OF FOREIGN TOURISTS
Month
2012
2013
2014
113627
115403
85953
66371
32600
29758
119865
127153
93175
72441
36302
33898
2015
January
February
March
April
May
June
106314
103220
75544
61335
30470
28280
July
August
Sep
Oct
Nov
Dec
Total
42977
45786
48577
51722
56666
59904
64518
69909
74710
81070
47440
51032
54245
57573
62599
63690
67702
71598
76119
82551
78833
83484
87720
89883
96155
95689
101909 108483 112206
121198
7,93,696 8,58,143 9,23,366 9,77,479 10,38,419
130463
132873
100156
76734
39583
35457
2016
136539
141143
107037
78099
37994
37368
2017
2018
150808
135089
107141
82633
49073
44040
167980
152003
120711
85493
45427
36746
72552
73736
54700
79957
107028
135113
10,91,870
68868
60121
44769
73263
99271
141755
10,96,407
% of
variation
11.39
12.52
12.67
3.46
-7.43
-16.56
-5.08
-18.46
-18.16
-8.37
-7.25
4.92
0.42
Source: tourism statistics 2018, Department of Tourism, Gvt of Kerala
Figure 1.5 MONTH WISE ARRIVAL
DETAILS OF FOREIGN TOURISTS
1200000
1000000
800000
600000
400000
200000
0
-200000
Conclusion
Kerala Tourism attracting international and
domestic tourists plays a significant role in
the economy of the State by contributing to
10% of the GDP and providing employment
to 1.5 million people in the State. With its
potential in creating employment and
enhancing production and productivity,
Kerala Tourism contributes to the
development of the State. Kerala
is
showing an increasing trend in foreign
tourist arrivals during the last few years.
According to the statistics for calendar year
2018, 0.42% growth in foreign tourist
arrivals and 6.35% growth in domestic
tourist arrivals was registered Inspite of the
great flood of 2018. We could back the
negative trend of tourist arrivals growth in
just 4 months with sustained innovative
tourism promotion activities post-floods.
During 2018, the foreign exchange earnings
from tourism in the State were `8764.46
crores, which shows an increase of 4.44%
over the last year. The total revenue Kerala
generated from tourism in the year 2018 is
worked out as `36258.01 crores.
Reference
1. „Kerala
Tourism Statistics 2018‟,
prepared by the Research and Statistics
division of the Department of Tourism,
2. https://www.keralatourism.org/tourismst
atistics/tourist_statistics_2018_book201
91211065455.pdf
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
A CROSS-SECTIONAL ANALYSIS ON THE INFLUENCE OF
VARIOUS COSTS OF AQUACULTURE ACTIVITIES ON
REVENUE FROM AQUACULTURE
Dr. K. PRADEEP KUMAR
Associate Professor
Government College, Attingal
Kollam, Ernakulam and Palakkad of Kerala,
INTRODUCTION
seelcted at random. The collection of quantitative
Commercial aquaculture refers to fish farming
data regarding revenue details was highly
operations, whose goal is to maximise profits,
complicated due to the lack of availability of
where profits are defined as revenue minus costs.
records. The data regarding cost and revenue have
Aquaculture is an economic activity that can
been collected in different stages of aquaculture
generate better returns to the farmers. Scientific
practice from stocking to harvesting. Cross-
aquaculture ensures better business with higher
evaluation has been done to ensure the reliability
growth opportunities. The role of aquaculture in
of the quantitative data.
producing high-grade animal protein for human
consumption is widely known.
TOOLS FOR DATA ANALYSIS
An economic
analysis into various aquaculture practices will be
This
study
explores
the
extent
of
useful in understanding the viability of this
variations in revenue due to the increased
practice. This paper attempts to analyse the
activities related to aquaculture.
influence of various costs of aquaculture
anlaysis is used to analyse the influence of
on
Regression
various cost of aquaculture activities on revenue
revenue from aquaculture.
from aquaculture. Ordinary Least Squares (OLS)
OBJECTIVE
modelling using Gretl software is attempted to
1. To analyse the influence of various
costs of aquaculture on revenue from
attain the objective of the study.
THE VARIABLES USED
aquaculture.
The various aquaculture activities are
METHODOLOGY
quantified through Labour involved, Seeds
Since the study is based on the sample
survey, the data has been collected from 300 fish
and crustacean farmers located in the districts of
stocked, Feeds used, and Fertiliser application.
Also, there are extraneous features associated
with aquaculture included in the category of'
„other expenses‟ like insurance premium, fuel
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
and electricity, lease rentals, interest on loan
Feed: Regular feeding ensures rapid growth of
taken, etc.
A small description of features
species across the culture period. Some farmers
specific to aquaculture is very useful in
practising modified traditional culture depend
understanding subsequent analysis.
only on wild feeds to ensure species growth. But
scientific aquaculture calls for regular feeding
Labour: Labour is essential to aquaculture from
stocking to harvesting. Small-sized farms use
family labour in all stages. Large-sized farms
depend
on
activities.
hired
labour
for
aquaculture
Regular labour is required for
activities like stocking, feeding, sampling, etc.
Likewise, at the time of harvesting also, hired
using
nutritional
feeds
available
through
governmental agencies like Matsyafed and feeds
manufactured by corporate organisations. There
is a wide variation in the use of feeds by the
farmers. Thus, feeding is another activity
considered here for measuring its impact on
revenue.
labour is used in many farms. The intensity of
culture in farms is also measured in terms of
Fertilisers: Fertilisation of pond is important
human involvement. In most of the farms in
for natural growth of planktons in the water
Kerala, farmers are following extensive to semi-
body. Planktons are the natural feeds for the
intensive culture. For extensive culture, more
species cultured.
labour is required, while, in semi-intensive
dung, coconut husks, etc., are used by farmers
culture, labour involvement is comparatively
practising aquaculture in Kerala. Some farmers
less. Thus, labour cost is the most important
even use manufactured fertilisers in the ponds to
element of cost incurred for generating revenue
ensure plankton growth. The use of fertilisers
from aquaculture.
helps the farmers to reduce the feed cost, as
Natural fertilisers like cow
natural feed is available in the pond after
Seed: The seed of the species to be cultured is
important in practising aquaculture.
fertilisation.
Farmers
depend on wild seeds and/or hatchery produced
Other Expenses: Other expenses involved in
seeds for farming. Now, due to a fall in natural
aquaculture activities include fuel and power,
availability of wild seeds, the farmers basically
lease rentals, if any, insurance premium, interest
depend on hatchery-produced seeds. The quality
on the loan taken for practising aquaculture,
of the seeds is very important for success in
transportation cost, etc. The cost of these
aquaculture.
The governmental agencies,
expenses varies in different farms. Thus, the
through their own hatcheries, provide seeds at
total of these costs, titled as other expenses, is
subsidised rates to the farmers, to encourage
considered for the purpose of anlysis to measure
aquaculture in Kerala. So the second major cost
its impact on revenue from aquaculture.
incurred for aquaculture is the seed cost.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
RESULTS AND DISCUSSION
It may be noted that in a particular case, it
is not necessary for all these factors (cost of
aquaculture activities) to exist together. From
Labour
Seed
Feed
−0.146181 0.107945 −1.354 0.1779
0.143723 0.0585259 2.456 0.0153 **
0.364909 0.0642427 5.680 <0.0001 ***
Fertiliser
−0.105608 0.0868359 −1.216 0.2260
OtherExpenses 0.250893 0.0444776 5.641 <0.0001 ***
the data on these variables, it may be noted that
Source: Primary Data
From the primary model it is inferred
these activities vary widely and only 142 cases
that the variables labour and fertiliser with
are reporting all the factors stated above.
negative coefficients are found to be
insignificant in predicting the dependent
Table 1 Descriptive Statistics
Variables
N
variable revenue. All other variables like
seed, feed and other expenses are found to
Revenue
300
Labour
300
be significant with positive coefficients
Seed
300
showing the marginal effect of each variable
Feed
249
on
Fertiliser
242
Other expenses
200
Valid N (list wise)
142
revenue
from
aquaculture.
specification of the model is proved through
the following table
Source: Primary Data
Table 3: Model Specification Summary
Ordinary Least Squares for Crosssectional Data (Using Gretl Software)
Mean dependent
var
Sum squared
resid
R-squared
To analyse the influence of various costs of
aquaculture to revenue from aquaculture a
model of Ordinary Least Squares is fitted for
the valid cases using Gretl software for cross
The
11.79056 S.D. dependent
var
0.761716
41.61905 S.E. of regression
0.553193
0.491270 Adjusted Rsquared
0.472567
F(5, 136)
26.26651 P-value(F)
1.68e-18
Log-likelihood
−114.3532 Akaike criterion
240.7063
Schwarz criterion 258.4413 Hannan-Quinn
247.9131
sectional data collected from sample. The
Source: Primary Data
The R2 and adjusted R2 are found to be
results after analysis are given below.
49.127 and 47.2567 respectively. The F
Model 6: OLS, using observations 1-300 (n
= 142)
Missing or incomplete observations
dropped: 158
Dependent variable: Revenue
Table 2. Regression Coefficients and its
signifiance
value for the model is found as 26.26651
with a p value less than .001. Thus the
model
suggests
that
explanatory
variables are sufficiently explaining the
variance in total revenue. Ramsey RESET
gives the following results
const
the
Coefficient Std. Error t-ratio p-value
7.65741 0.655990 11.67 <0.0001 ***
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
RESET test for specification Null hypothesis: specification is adequate
Test statistic: F(2, 134) = 2.57791
with p-value = P(F(2, 134) > 2.57791) =
0.0796949
Source: Primary Data
From the RESET test for specification, the
null hypothesis is accepted since the
significance value is greater than 0.05. Thus
the fitted model‟s specification is seems to
be adequate.
CONCLUSION
The special feature of aquaculture that
Reference
1. Field, Andy, Discovering Statistics
using SPSS, SAGE Publications,
2009 pp 187-263
2. Raju M.S., Economic Analysis of
Different Aquaculture Systems in
Kerala – A Production Function
Approach,Unpublished Ph.D thesis,
Cochin University of Science and
Technology (CUSAT),1997.
3. Pradeep Kumar K., Production and
Marketing of Aquaculture Products
in Kerala, Ph.D thesis published by
Abhijeet
Publications,
New
Delhi,2014
discriminates itself from capture fisheries is that
harvesting can be organised according to market
demand in terms of quantity, size, etc. To
explore the influence of various activities on
revenue from aquaculture, OLS model using
Gretl software was developed for the five
important cost variables. The models provide
significant R2 value in all cases with positive
coefficients. From the regression coefficients the
three most important variables determining
revenue are identified as Seed, Feed and other
expenses.
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
LIST OF PARTICIPANTS
Sl. No.
1
2
3
4
5
6
7
8
9
10
11
Name, Designation and Institutional Address
Dr. LAKSHMANAN M.P
Assistant Professor of Commerce
Government College
CHITTOOR Phone No. 9249214643
Dr. MOHANADASAN T
Assistant Professor of Commerce
Victoria Government College
Palakkad Phone No.9249758697
Dr. JAYARAJU V.
Associate Professor of Commerce
Iqbal College, Peringamala Phone No. 9447958248
AJEESH A.
Assistant Professor of Commerce
G.P.M. Government College
Manjeswaram Phone No.9422441007
LEKSHMI PRAKASH
Assistant Professor of Commerce
G.P.M. Government College
Manjeswarm
VIJAYAN K
Assistant Professor of Commerce
Government College,
Nedumangad Phone No. 9496101019
BINOY S.
Assistant Professor of Commerce
S.N. College,
Chathannoor Phone No. 9846151302
SIMU RAJENDRAN
Assistant Professor of Commerce
S.N. College,
Kollam Phone No 9074491741
Dr. SUMESH G.S
Assistant Professor of Commerce
M.G. College,
Thiruvananthapuram Phone No.9447855544
Dr. PRADEEP KUMAR N.
Assistant Professor of Commerce
M.G. College,
Thiruvananthapuram Phone No. 9847888777
Dr. SREEDEVI S.R.
Assistant Professor of Commerce
Government Arts College
Thiruvananthapuram
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Sl.No
12
13
14
15
16
17
18
19
20
21
22
23
24
Name, Designation and Institutional Address
Dr. LEKSHMI P
Assistant Professor of Commerce
V.T.M. N.S.S. College
Dhanuvachapuram
REJANI R. NAIR
Assistant Professor of Commerce
Government College
Nedumangad
INDURAJANI R
Assistant Professor of Commerce
Government College
Nedumangad
SARITHA G.S.
Assistant Professor of Commerce
N.S.S. College,
Niamel
Dr. ASWATHY P.
Assistant Professor of Commerce
N.S.S. College,
Neeramankara
Dr. S. KRISHNAVENI,
Assistant Professor of Commerce
Government College for Women
Thiruvananthapuram
Dr. KALARANI T.G
Assistant Professor of Commerce
V.T.M. N.S.S. College
Dhanuvachapuram
SALINI R.S
Assistant Professor of Commerce
UIT, Vakkom
ANEETA VICTOR
Assistant Professor of Commerce
UIT, Vakkom
ARCHANA S
Assistant Professor of Commerce
TKM Institute of Management, Kollam
SUMAN S.
Research Scholar
Department of Commerce, University of Kerala
DEVI KRISHNA
Research Scholar
S.N.College, Kollam
SEREENA A
Research Scholar
M.G. College
Thiruvananthapuram
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
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25
26
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Name, Designation and Institutional Address
ANCY JOHN
Research Scholar
M.G. College
Thiruvananthapuram
MAYA BABU B.P
Research Scholar
M.G. College
Thiruvananthapuram
ATHENA PRINCE
Research Scholar
M.G. College
Thiruvananthapuram
SHIYAS I.S
Research Scholar
IMG, Thiruvananthapuram
REJITHA Y.S
Research Scholar
KSMDB College
Sasthamkotta
ANU G.S
Research Scholar
University of Kerala
ILYAS P.C
Research Scholar
Department of Commerce
University of Kerala
ANN MARY ALEXANDER
Research Scholar
Department of Commerce
University of Kerala
VISHNU S. KUMAR
Research Scholar
Department of Commerce
University of Kerala
RAHUL R. KURUP
Research Scholar
Department of Commerce
University of Kerala
SRUTHI S.G
Research Scholar
Department of Commerce University of Kerala
ASEEM R.
Research Scholar
Govt. Arts College, Thiruvananthapuram
IRSHAD V.
Research Scholar
S.N. College, Kollam
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Sl.No
38
39
40
41
42
43
44
45
Name, Designation and Institutional Address
VINITHA V.K
Research Scholar
M.G. College
Thiruvananthapuram
SREEJI S.L
Research Scholar
Institute of Management in Government (IMG)
Thiruvananthapuram
AFRA NAHAN M.T
Research Scholar
Department of Commerce and Management Studies
University of Calicut
NAFEESATHUL THANSILA BEEVI
Research Scholar
Department of Commerce and Management Studies
University of Calicut
RESMI R.
Research Scholar
M.G. College,
Thiruvananthapuram
JINU L
Research Scholar
Institute of Management in Government (IMG)
Thiruvananthapuram
GOPISH G.
M.Phil. Scholar
Department of Commerce
University of Kerala
ANN MARY VARGHESE
M.Phil. Scholar
University College,
Thiruvananthapuram
LIST OF PARTICIPANTS FROM GOVERNMENT COLLEGE, ATTINGAL
Sl. No
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Name, Designation and Institution
SUNIL S, Head of the Department
Assistant Professor of Commerce
Government College, Attingal
Dr. K.PRADEEP KUMAR (Coordinator)
Associate Professor of Commerce
Government College,
Attingal
Dr. SUNILRAJ N.V (Co-coordinator)
Assistant Professor of Commerce
Government College
Attingal
Government College, Attingal
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Name, Designation and Institutional Address
Dr. ANITHA S
Assistnat Professor of Commerce
Government College, Attingal
Dr. SAJEEV H
Assistant Professor of Commerce
Government College, Attingal
Dr. SARUN S.G
Assistant Professor
Government College, Attingal
MANIKANTAN G.
Assistant Professor
Government College, Attingal
SHANIMON S
Assistant Professor
Government College, Attingal
Dr. BINU R
Assistant Professor of Commerce,
Government College, Attingal
KRIPA M DAS
Research Scholar
Department of Commerce,
Govt.College, Attingal
PRAGEETH P
Research Scholar
Department of Commerce,
Govt. College, Attingal
ANSA S
Research Scholar
Department of Commerce,
Govt. College, Attingal
KAVITHA S
Research Scholar
Department of Commerce,
Govt. College, Attingal
JENCY S
Research Scholar
Department of Commerce,
Govt. College, Attingal
ABHIRAMI S.R
M.Com III Semester
Govt. College, Attingal
AKHIL A.U.
M.Com III Semester
Govt. College, Attingal
AKHIL S.K
M.Com III Semester
Govt. College, Attingal
Government College, Attingal
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65
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ANJALI RAJAN M.R.
M.Com III Semester
Govt. College, Attingal
ANOOP R. NAIR
M.Com III Semester
Govt. College, Attingal
ARYA KRISHNANA R.S
M.Com III Semester
Govt. College, Attingal
ATHIRA P.R
M.Com III Semester
Govt. College, Attingal
FATHIMA M. ASHRAF
M.Com III Semester
Govt. College, Attingal
KARTHIKA S.R.
M.Com III Semester
Govt. College, Attingal
MAHITHA M.S.
M.Com III Semester
Govt. College, Attingal
SANDHYA S.
M.Com III Semester
Govt. College, Attingal
71
SHEFNA S.
M.Com III Semester
Govt. College, Attingal
72
SREEJA M.N
M.Com III Semester
Govt. College, Attingal
SUBIMOL B.S
M.Com III Semester
Govt. College, Attingal
SWATHY K.S
M.Com III Semester
Govt. College, Attingal
73
74
75
VAISHNAVI V.S
M.Com III Semester
Govt. College, Attingal
76
G.S. SACHIN
M.Com III Semester
Govt. College, Attingal
GREESHMA G.P
M.Com III Semester
Govt. College, Attingal
77
Government College, Attingal
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ABHILA M. DAS
M.Com I Semester
Govt. College, Attingal
79
AMRITHA G.M.
M.Com I Semester
Govt. College, Attingal
80
ARYA A.J.
M.Com I Semester
Govt. College, Attingal
81
ARYA ASHOK
M.Com I Semester
Govt. College, Attingal
82
ASHA B
M.Com I Semester
Govt. College, Attingal
83
BHAVYA VIJAYAN
M.Com I Semester
Govt. College, Attingal
84
FATHIMA KALAM
M.Com I Semester
Govt. College, Attingal
85
FATHIMA S.
M.Com I Semester
Govt. College, Attingal
86
SHANI B.
M.Com I Semester
Govt. College, Attingal
87
SILPAMOL P.M
M.Com I Semester
Govt. College, Attingal
88
SNEHA K.N
M.Com I Semester
Govt. College, Attingal
89
THASNIM S.
M.Com I Semester
Govt. College, Attingal
Government College, Attingal
Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019
Research and Post Graduate Department of Commerce
Government College, Attingal
Government College, Attingal