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The Basics of Financial Econometrics: Tools, Concepts, and Asset Management
Applications
Article in Quantitative Finance · November 2015
DOI: 10.1080/14697688.2015.1080486
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Quantitative Finance
ISSN: 1469-7688 (Print) 1469-7696 (Online) Journal homepage: http://www.tandfonline.com/loi/rquf20
The Basics of Financial Econometrics: Tools,
Concepts, and Asset Management Applications
K. Surekha Rao
To cite this article: K. Surekha Rao (2015) The Basics of Financial Econometrics: Tools,
Concepts, and Asset Management Applications, Quantitative Finance, 15:11, 1773-1775, DOI:
10.1080/14697688.2015.1080486
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Published online: 14 Sep 2015.
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Date: 21 November 2015, At: 09:03
Quantitative Finance, 2015
Vol. 15, No. 11, 1773–1775, http://dx.doi.org/10.1080/14697688.2015.1080486
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Book review
© 2014, Wiley
The Basics of Financial Econometrics: Tools, Concepts,
and Asset Management Applications, by Frank J.
Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev and Bala
G. Arshanapalli with Markus Hoechstoetter, Wiley (2014).
ISBN 978-1-118-57320-4.
Econometrics has traditionally been the application of statistical methods to the building of models for economic policy and forecasting and for the empirical verification of
economic theories. When one applies these models and
methods of econometrics to financial data, one is entering
the realm of financial econometrics.
Since the turn of the century, there has been a remarkable
growth in the field of financial econometrics to the extent
that most new developments in the theory of econometrics in
the context of time series data were largely the result of its
application to financial data. Developments of new
techniques and technologies have given a new dimension to
the analysis of financial data in the last couple of decades.
This led to new books on financial econometrics and time series analysis of financial data. Most of these early books on
financial econometrics were naturally inclined towards
developing the latest theoretical developments and
presenting advanced techniques. Unfortunately, because of
the relatively cutting-edge nature of the new work, several of
these good books on financial data analysis modelling and
financial econometrics (such as those by Campbell et al.
(1996), Gourieroux and Jasiak (2001), and those written by
permutations and combinations of subsets of the authors of
the book under review) remained inaccessible to most practicing asset managers and security analysts.
The Basics of Financial Econometrics makes a welcome
change in this landscape. This book explains a lot of financial and statistical jargon in a way accessible to the layman
and provides basic tools and techniques that can be used
with most available software without the need for the user
to learn complex mathematical and statistical formulae. The
book, rather than starting with the usual heavy dose of
econometric theory, tries to initiate the reader with real
problems and helps build necessary skills to apply the
methodologies described in the book to real-world financial
problems. Even the most uninitiated, non-quantitative
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1774
Book review
applied financial economist will feel comfortable with the
intuitive style.
The emphasis on practical applications is reflected in the
plan of the book. Roughly half of the 400+ pages are
devoted to explaining concepts from finance, statistics and
econometrics, including 90 pages of appendices on different
topics. The second half of the book—the part that makes it
most valuable—has excellent examples that apply econometric techniques to real financial data, providing model
specification, parameter estimation and a detailed stepby-step explanation that is most helpful for applied financial
analysts and fund managers.
Here is a detailed analysis of why this book is a very
good introduction to applied and basic financial econometrics. Chapter 2 on the simple linear regression model has
two excellent examples; one dealing with estimating the
characteristic line of a fund or a stock and the second
example deals with hedging. In the second example, even a
non-expert in futures can follow the step-by-step guide in
order to calculate the minimum risk hedge ratio and the
number of contracts to be sold. The authors explain the
terminology: hedging, hedge ratio and number of equivalent
market index units, and then explain in detail why a particular hedge was not a perfect hedge. After reviewing this
example, the reader can appreciate not only why hedging
requires the use of econometric analysis but also why it is
not as simple or straightforward as it is often presented in
textbooks.
The third chapter develops multiple regression models.
They are then applied to estimating empirical duration, predicting Treasury yields, estimating Sharpe benchmarks for
evaluating fund manager investment style and testing the
market efficiency hypothesis. In each case, the authors use
financial market data and provide estimates for the models
and their interpretation. The limitations of the models are
highlighted.
Chapters 4, 5 and 6 are extensions of linear models in a
variety of directions. They discuss issues of multicollinearity and choice of variables in model building, specification
testing and the more advanced tools that should be in the
econometric toolkit of a portfolio manager and financial
analyst. The authors introduce just about enough tests for
verifying the assumptions and the right kind of variables
that can be carried out by practically any standard econometric software or a statistical regression package. In a
model to predict corporate bond yield spreads, for example,
you would need to know how to capture the effect of the
bond ratings in addition to the coupon rate, the coverage
ratio and other factors that are known to impact spreads.
The fundamentals of time series analysis are the subject
of Chapter 5 which covers the properties of times series
and their decomposition with applications to the returns of
the S&P 500 index. To deal with such situations in multiple
regression models with categorical variables, the authors
introduce the use of dummy variables in Chapter 6. However, there is only a limited discussion of dependent categorical variables that necessitate logit and probit models.
This may be partly because the most appropriate estimation
method used in such situations is the maximum likelihood
method, a method covered later in the book.
Most financial data do not fit into the ideal bell-shaped
(i.e. normal) data distribution. To the contrary, real-world
financial data are skewed or fat tailed, requiring that a fund
manager or analyst move beyond the usual regression models with mean predictions to the world of quantile regressions. This topic, covered with excellent description and
motivation, is detailed in Chapter 7 along with two realworld examples. The first example shows how to identify a
portfolio manager’s investment style; the second example
explains how management would determine the mixture of
debt and equity for a corporation’s capital structure—this,
by the way, is one of the few examples not from the field
of asset management but from financial management.
If the data have outliers and you are looking for robust
results, you have to move beyond linear regression to
robust regression estimation that uses Huber or Tukey
weighting functions to compute weighted least squares.
Chapter 8 describes all the necessary steps to obtain a
‘resistant beta’.
Econometric methods have been extensively applied to
the analysis of financial time series data. Chapters 9, 10
and 11 deal with these topics. In Chapter 9, the authors provide an extensive discussion with examples of autoregressive, moving average and autoregressive moving average
models for forecasting returns. If the data show some pattern, technical analysts can follow a step-by-step guide for
the entire process of identification, estimation, diagnostic
testing and forecasting. A nice feature of this chapter is the
way the authors introduce vector autoregressive models,
leading into the next chapter, Chapter 10, on cointegration.
It is generally well known that there are common trends
among economic and financial variables, such as long-term
relationships and occasional financial bubbles. This chapter
explains how and when a financial analyst and a fund manager can systematically test for non-stationarity, random
walk, order of lags, unit roots and cointegration (both
Engle–Granger and Johansen–Juselius). Using Japanese
stock index data, the authors help you identify problems of
spurious regressions and obtain empirical results for the
dividend growth model using S&P 500 index data. Their
study of European stock price linkages using the cointegration methodology examines short-term and long-term
dynamics. This is a perfect example of how this book
explains the techniques meticulously, and at the same time
describes nuances of various interpretations of tests and
results.
No book on financial models can be complete without
the study of the generalized autoregressive conditional
heteroscedastic model and its various forms because the
volatility of future returns is inherently asymmetric. This
topic, covered in Chapter 11, provides enough details for
the reader to acquire great insight into this topic without
being overwhelmed with all the technicalities.
Chapter 12 describes a key topic in asset management that
fund managers need to understand: identification of factors
that drive asset returns. Although portfolio theory and asset
pricing are topics well developed in finance textbooks, the
authors discuss factor models from a statistical perspective.
They suggest moving from the theory of factor models to the
identification of factors using two econometric methods:
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Book review
factor analysis and principal components. The differences
between these two techniques are explained. Chapter 13
describes advanced methods of model estimation such as the
method of moments, instrumental variable regressions and
maximum likelihood estimation methods.
The last two chapters are focussed on the practice of
financial econometrics and provide an extensive discussion
of implementation issues. These are topics rarely found in
books at this level. Once the reader has learnt about econometric techniques described in the earlier chapters, the next
important step in practice is to select the model. Chapter 14
explains how this can be done, covering topics such as
machine learning, over-fitting, survivorship bias and data
snooping. The closing chapters also discuss what is modelling risk and how this can be mitigated. Once a model
has been selected using the framework and directions provided in Chapter 14, the next chapter takes the reader
through how financial econometrics can be used to formulate and implement investment strategies. The section on
estimation and modelling of expected returns, the significance of out-of-sample prediction and need for minimizing
tracking error provides a candid approach to real-life investment strategies. The discussion of the three phases of the
quantitative research process given in this last chapter is a
must-read for all practitioners.
Non-specialists in financial econometrics are currently
faced with introductory books such as Brooks (2014) or
books such as Gourieroux and Jasiak’s (2001), which are
advanced reference books with a synthesis of financial theory and with statistical methodology suited only for those
with considerable mathematical and statistical expertise.
The book under review is a good combination of a reference book for practitioners and a textbook for undergraduate students (without the coloured graphs, the practice
problems and the solutions manual). It also strikes a happy
medium between theory and practice of asset and risk management. In one way, it will remind many readers and
practitioners of econometrics the readability and likeness of
the book on Basics of Econometrics by Gujarati (1978,
2010) and in another way, it is quite like the book on Practice of Econometrics by Berndt (1996), a book that showed
how to apply econometric techniques to a variety of empirical problems and to interpret the results.
Now, I address some of the drawbacks.
The number of authors (five) sometimes creates problems
of unevenness. Fortunately, many of them have written
extensively on the topics covered in this book—especially
Fabozzi, who is a prolific author of over 265 research papers
and two dozen books, several on finance. Overall, the book
is fairly consistent in its overall style and presentation. For
example, every chapter begins with a few learning outcomes
enumerated very clearly under the title ‘After reading the
chapter you will understand …’ and at the end of every
chapter, there are ‘Key Points’ that provide a nice executive
summary report that help you recollect and appreciate what
all you have just learned and connect back to the outcomes.
For all this, there are times when the reader feels sudden
shifts in depth and breadth of certain topics and some
disconnect because of distinct styles. For example, when
reading Chapter 2 (Simple Linear Regression), Chapter 7
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1775
(Quantile Regressions) and Chapter 10 (Cointegration), there
seems to be a more lucid explanation of concepts and
description of results than in other parts of the book. While
reading these chapters, the reader feels that someone is holding her hand all the way from beginning till the end of solving the problem and interpreting it in simple terms. And
then there are few other chapters on more advanced topics
such as Chapter 8 (Robust Regressions) and Chapter 12
(Factor Analysis and Principal Components Analysis),
where the style is more rooted in mathematical formulae and
theory, even though it is still presented at a basic level. At
such times, the reader can feel a bit left on her own, and she
must be aware of basic results in statistics and matrix algebra that would not have been required in the more tutorial
chapters. As a consequence, the reader can feel like hitting
some road bumps on an otherwise very smooth ride of learning and practicing the basic tenets of financial econometrics.
The second is the complete omission of any kind of
reference to Bayesian methods even in those non-standard
situations like testing for unit roots, where classical methods
have less power or model selection, where there is no unified approach. For a pragmatic Bayesian like myself, I
would have liked to have seen at least a chapter devoted to
Bayesian methods. This would have given a choice to the
practitioners in non-standard situations. This was even more
surprising because two of the five authors, Rachev and
Fabozzi, have co-authored a book titled Bayesian Methods
in Finance (2008).
Overall, this book is a useful introduction to the theory
and practice of financial econometrics. It is refreshing and
fulfils a long-standing need for an introductory-level book
on the econometrics of applied portfolio management and
financial analysis. In doing so, it helps to close the gap
between portfolio selection theory and real-world asset
management.
References
Berndt, E.R., Practice of Econometrics: Classic and Contemporary, 1996 (Addison-Wesley Longman Incorporated: Reading,
MA).
Brooks, C., Introductory Econometrics for Finance, 3rd edition,
2014 (Cambridge University Press: Cambridge, UK).
Campbell, J.Y., Lo, A.W. and MacKinlay, A.C., The Econometrics
of Financial Markets, 1996 (Princeton University Press:
Princeton, NJ).
Gourieroux, C. and Jasiak, J., Financial Econometrics: Problems,
Models and Methods, 2001 (Princeton University Press:
Princeton, NJ).
Gujarati, D., Basic Econometrics, 1978, 2010 (McGraw-Hill:
New York, NY).
Rachev, S.T., Hsu, J.S.J. Bagasheva, B. S. and Fabozzi, F. J.,
Bayesian Methods in Finance, 2008 (Wiley: Hoboken, NJ).
K. Surekha Rao
School of Business and Economics, Indiana University
Northwest, Gary, IN, USA
© 2015, K. Surekha Rao