BE372 - Coursework Assignment
Instructions: This coursework assignment must be submitted electronically via FASER
by the due date and time. If you have any problems with submission, then contact one of
the module lecturers. When submitting your coursework assignment you should provide
an answer to each question, along with the relevant output from your chosen statistical
software. There is no word limit for this assignment but the best submissions will be
clear and concise, ensuring that only relevant information is highlighted.
Question 1
The Excel spreadsheet ”AssignmentQ1data.xls” contains data for the returns of FTSE
100 and its 5-minute realised variance for the period from 04-01-2000 to 29-12-2017. This
question draws heavily from material across different weeks but the code that you need
to use is very similar to the ones used in our Computer classes. You need to do the
following tasks:
a) Import both variables above into software of your choice and provide a short summary on the FTSE 100 returns using graphical analysis.
b) Check for stationarity and if needed make your variables stationary.
c) Do an out-of-sample (recursive) forecasting exercise for the returns of FTSE 100,
for h = {1, 7, 14} for the last 100 observations using the following models:
i) AR(1), AR(2), AR(3), MA(1), MA(2), MA(3) and an ARMA(p, q) of your
choice.
ii) Which model would you prefer? Use MAE and RMSFE.
d) Test for ARCH effects. State clearly the test and results.
e) Estimate a GARCH(p, q) model using the best performing ARMA(p,q) model
as the conditional mean. Do an out-of-sample (recursive) forecasting exercise for
h = 1, where you need to produce conditional variance forecasts for the last 100
observations. Use RMFSE to select the optimal order for the GARCH model. Use
both yt2 (squared returns of FTSE 100) and Realised variance as proxies to compute
RMFSE.
Question 2
The Excel spreadsheet AssignmentQ2data.xlsx contains data on four time series, x1 , x2 ,
x3 and x4 . This question is very similar to that posed in Computer Class 9 but you are
now dealing with four variables instead of three.
a) Determine whether these variables are stationary or non-stationary using the standard ADF test. You should allow for a linear trend in the data with lag selection
determined automatically using the standard settings in Eviews.
b) Estimate a VAR(1) model for the four variables, again allowing for a linear trend.
Like in the previous computer class, use Eviews’ in-built sequential modified LR
test statistic to determine an appropriate order for a VAR model for these variables.
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c) Perform the Johansen test for cointegration imposing the lag order identified in
Question 2. Allow for a linear trend in the data by allowing for an intercept in the
cointegrating equation and test VAR. How many cointegrating relationships do you
find?
d) If in the previous question the Johansen procedure determined there to be four
cointegrating relationships what would you have concluded?
e) Estimate the VECM model implied by the results from your Johansen test for
cointegration in Question 3. Write out the estimated long run relationship(s) and
the equations governing the short run dynamics of the system. Discuss your results.
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