This coursework (individual not a group work) counts for 50% of your overall assessment for this module. Please submit your completed answers (a maximum of 7 A4 pages excluding the apprendix) to the unit Moodle page. Please also submit all your Eviews workings copied in the appendix with your coursework.
This coursework exercise should be completed individually. This coursework requires significant time commitment.
Learning Outcomes
This coursework assignment assesses the following learning outcomes (LO) of the module:
1. Assess the properties and limitations of econometric methods as applied to the analysis of financial data.
2. Formulate and test econometric models to examine different forms of financial data.
3. Evaluate the results of econometric analysis.
Word limit:
The maximum word count for this exercise is 1500 words which includes everything between the start and completion of the answer, excluding tables, figures, foot - and endnotes, and appendices, but including any in-text citations.
References and additional material:
In answering the questions below, you must consult the relevant chapters of the recommended book Introductory Econometrics for Finance by Chris Brooks and my lecture handouts, my Eviews based lab sheets and the help option in Eviews
Help:
If you require any clarification regarding this coursework assignment, please utilise the Q&A forum on the Module Moodle page to reach me. Please also make full use of the help facility in Eviews as well.
Software:
Eviews is the required software to carry out this coursework assignment.
Data:
You have been provided data about one stock downloaded from Yahoo Finance. The data file containing your data is available on the Module Moodle page. The Excel file name is your allocated stock name. Import your data into Eviews.
In answering the questions below, you may wish to consult the help option in Eviews.
1. For your series, in Eviews use the Genr option to calculate (i) the log of the price series, e.g. e=log (Adj Close), and (ii) the daily log returns (e.g. r=log(Adj Close)-log(Adj Close(-1))).
i. Examine the descriptive statistics for both e and r. What do you conclude about the distributions of e and r? Is e normally distributed? Is r normally distributed? Explain why/why not?
ii. Obtain the correlograms, and examine the autocorrelations and partial autocorrelations for both e and r. What do you conclude about the behaviour of e and r? Are they stationary/non-stationary?
iii. Are your conclusions about stationary/non-stationary of e and r confirmed by appropriate unit root tests?
2. Estimate and select an appropriate ARMA (p,q) model for e. In selecting your preferred model, use the information provided by:
i. The estimated coefficients (and their t-statistics or p values)
ii. Serial correlation in the residuals
iii. Information criteria.
Produce a summary table similar to this.
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