Unformatted Attachment Preview
University of Essex
ESSEX BUSINESS SCHOOL
(Empirical Methods in Finance)
The assessment for BE953 is by this coursework and a Final Examination. This piece of coursework is
worth 50% of the overall assessment of BE953. The requirements for this coursework are as follows:
The coursework consists of data manipulation, analysis and interpretation. Although you may discuss
the project with others, the coursework must be written up individually. You may receive
reduced or no marks if there are strong similarities between the work handed in by two or more
All questions are to be answered.
The word count of the project must be printed on the first page of the coursework. The
maximum word count is 2000. The project should be double-spaced and word-processed.
Your project should include a title page and a bibliography, which includes the full reference for all
articles, books and other sources you have cited in the body of the text. The bibliography (and any
footnotes) need not be included in the word count.
EViews output should NOT be pasted directly into the project. You should present your EViews
equation estimation output as it would be in published academic papers. (Look at some papers –
sometimes output is in Tables, sometimes as estimated equations with s.e./t stats/p-values in brackets
under the corresponding coefficient, together with appropriate diagnostic statistics and their p-values).
Note that your coursework is to be submitted via FASer. The coursework should be uploaded to
FASer by 09:00 on Saturday 30 January 2021. You should also upload your EViews workfiles for
Questions 1 and 2.
More information concerning late submission of coursework, can be found here:
YOU MUST READ THE INFORMATION WHICH FOLLOWS:
In submitting coursework online it must be assumed that you have read and understood the following guidelines
about plagiarism. Furthermore in doing so you are agreeing to your work being monitored by the JISC
Plagiarism Detection System if a lecturer should deem it necessary to do so.
University Regulation 6.12 & 6.13 states that
6.12(a) It is an academic offence for a student to cheat in any examination, or in any other submitted part of his
or her University work, whether or not such work is formally assessed. "To cheat" includes:
(i) to copy the work of another candidate or otherwise communicate with another candidate in an
(ii) to introduce any written, printed or electronically-stored information into an examination, other
than material expressly permitted in the instructions for that examination;
(iii) to use the work of others (whether in written, printed or some other form) without
acknowledgement, where a judgment is made that the work has been the result of serious
negligence or of intention to deceive;
(iv) to repeat work previously submitted for a different assessed assignment without full
acknowledgement of the extent to which that previous work has been used.
(b) It is an academic offence for a student knowingly to assist another student to cheat in any examination, or
in any other piece of work, the mark for which will count either towards the student's result for the year, or
towards his or her final degree classification.
(c) Allegations of academic offences involving cheating shall be dealt with in accordance with the Progress
Procedures as determined by the Senate. Previous offences shall be taken into account.
6.13 In submitting any piece of University work (e.g. dissertation, thesis, essay or report) a student shall
acknowledge any assistance received or any use of the work of others.
The data for question 1 can be found on Moodle under the heading "OilPrices2018.xls", showing the
spot and futures prices for Crude Oil. For question 2, each student will be allocated a company from the
FTSE 100 and will need to download price data from Yahoo Finance.
An estimable linear regression can be specified as:
𝑠𝑡 = 𝛽0 + 𝛽1 𝑓𝑡−1 + 𝑢𝑡
where 𝑓𝑡 and 𝑠𝑡 are the natural logarithms of 𝐹𝑡 (the nearby futures price) for the Oil contract traded on
NYSEX and 𝑆𝑡 (the spot price). Note that 𝑢𝑡 is an error term. Import the data file “OilPrices2018.xls”
into EViews. The data is sampled monthly from January 1986 to August 2018.
a) Plot 𝐹𝑡−1 and 𝑆𝑡 on the same graph. Indicate on the graph any major economic events. Additionally,
using an Augmented Dickey-Fuller test and selecting the relevant lag length using SBIC, assess whether
𝑓𝑡 and 𝑠𝑡 are unit root processes.
b) Explain the term spurious regression. Why might regression (1) be spurious?
c) Using the Engle-Granger 2-step method, assess whether 𝑓𝑡−1 and 𝑠𝑡 are cointegrated. If so, what does
d) Discuss the economic rationale behind the result in 1(c) explaining terms like unbiasedness and market
efficiency. Also briefly comment on how this result compares with the relevant economic literature.
Some useful references (which can be downloaded electronically from the library) include:
Chow, Y-F. (2001), “Arbitrage, Risk Premium, and Cointegration Tests of the Efficiency of Futures
Markets”, Journal of Business Finance and Accounting, 28: 693- 713.
Kellard, N., Newbold, P., Rayner, A. and Ennew, C. (1999), “The Relative Efficiency of Commodity
Futures Markets”, Journal of Futures Markets, 19: 413-432.
e) Assuming that the series, 𝑓𝑡−1 and 𝑠𝑡 , are cointegrated, formulate and estimate a general ECM (Error
Correction Model) and using appropriate information criteria and diagnostic tests, ‘test down’ to a more
parsimonious model. Briefly comment on the properties of your final ‘preferred’ model including
discussion of short and long run elasticities. Do the standard diagnostic tests indicate any problems?
Read the following journal article (available on Moodle and in the library):
Sun, Q. and Tong, W. (2010), “Risk and the January Effect”, Journal of Banking and Finance, 34: 965974.
Download your allocated FTSE 100 company price data from Yahoo Finance. You should choose a daily
frequency from the earliest start date to 30/11/2019. Using the adjusted closing price (pt) log returns can
be formed by constructing the variable:
𝑟𝑡 = ln (𝑝
a) Using an appropriate test, assess the possibility of ARCH effects in rt. When estimating volatility of
returns, why might a GARCH type model be preferred to simply calculating the historic standard
b) Estimate an appropriate model specification for returns using (i) a GARCH (1,1) model and (ii) a
GARCH-in-mean (1,1) model. Express each estimation in the appropriate equation form and interpret the
c) Using your allocated data estimate:
(i) model (1) on page 967 of Sun and Tong (2010)
(ii) model (2) also on page 967.
If the lagged returns variable is not significant then delete it from your equations for returns. Express
each estimation in the appropriate equation form and interpret the coefficients.
Given your daily data, creating a dummy variable for observations in January can be done in a number of
i. Setting JAN = 0 and then editing the values for January
ii. Use a new page in workfile by:
Right clicking new page >Specify by frequency/range at bottom of workfile;
Select Monthly as frequency;
Define a dummy for January, JAN, say using the @seas command;
Select original (daily) page in workfile and paste JAN.
d) On the basis of the values of appropriate information criteria, which of your 4 models (i.e., Two
models from (b) and two models from (c)) would you prefer? Explain.
e) Estimate your “preferred” regression from 2(d) above using observations up to and including
31/10/2019. Forecast the annual volatility of returns over the next twenty-one trading days. Briefly
comment on the forecast you obtain.
Total marks: 100