Economics and Finance Subject Group
Order ID 53563633773 Type Essay Writer Level Masters Style APA Sources/References 4 Perfect Number of Pages to Order 5-10 Pages Description/Paper Instructions
Financial Econometrics (Part 2)
Aim:
To introduce students to stationary and non-stationary time series models (ARMA and
ARIMA, respectively). To examine stochastic volatility via ARCH and GARCH modelling. To
examine time series features and stochastic volatility properties simultaneously in a single
equation model. To demonstrate the application of the above type(s) of modelling to financial
data. To use these models for estimation, inference and forecasting.
Syllabus Outline:
Univariate linear stochastic models: ARMA and ARIMA modelling, estimation and forecasting
using ARMA and ARIMA models, application to macroeconomic and/or financial data.
Modelling stochastic volatility: ARCH and GARCH processes. ARCH in mean model,
Asymmetric ARCH models, TARCH and EGARCH model. AR(I)MA models with (G)ARCH
errors. Applications to financial data.
You are expected to read the relevant material in advance.
The computer software for this course is Eviews (5.0 or above), which is available on the
student network. For most of the exercises you will be required to collect data from the
BANKSCOPE, DATASTREAM databases.
Indicative Reading:
Brookes, C., Introductory Econometrics for Finance Cambridge University Press 2008
HARRIS, R and Sollis, R., Applied Time Series Modelling and Forecasting Wiley 2003
PINDYCK, R & RUBENFELD D Econometric Models & Economic Forecasts McGraw & Hill 3rd
Edition
Harvey, A. C. Time Series Models, Harvester Wheatsheaf 1993
Mills, T. C. Time Series Techniques for Economists, Cambridge 1990
Mills, T. C. The Econometric Modelling of Financial Time Series, 1993 Cambridge.
Hamilton, J. D. Time Series Analysis, 1994 Princeton
Cuthbertson K, Nitzsche D., Quantitative Financial Ecnomics, 2004 WileyAssignment
This will involve answering a set of short questions based on your analysis of a data-set. The
assignment is worth 25% of the total assessment. In addition to the above assignment there
will be a final unseen examination (worth 50% of the final marks).
Professor Shabbar Jaffry (R4.01)Financial Econometrics (part 2)
Assessed Coursework
Submission Deadline: 27 March 2020 (23:55 unit Moodle page)
This coursework (individual not a group work) counts for 25% of your overall assessment for this unit.
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 requires you to collect 4 sectors of the economy (e.g. real estate, banking, general
retail etc.) daily (daily observations excluding weekends and holidays) indices (for London exchange
market or for other market if the data is available) for the allocated country from the Eikon. The
length of each series should not be less than 10 years. In answering the questions below, you may
wish to consult the help option in Eviews.
You are expected to have collected data for the assignment by 15 th February and estimations by Mid-
March.1. Choose one of the four series on which to conduct your analysis. For your chosen series, in
Eviews use the Genr option to calculate (i) the log of the series, e.g. e=log(your chosen series),
and (ii) the daily log returns (e.g. r=e-e(-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,
explain how you use the information provided by:
i. The estimated coefficients (and their t-statistics);
ii. Ljung-Box Q-statistics for autocorrelation in the residuals; and
iii. AIC and SBC information criteria for choosing between alternative models.
iv. Carry out forecast of e for 100 observations based on your chosen model and
comment on your results.
In the following section (v), use 100 less observations from the full sample (e.g. if the full
sample was 2000 then only use 1900) available to you.
v. Carry out forecast (out of sample) of e for 100 observations based on your chosen model and
comment on your results.
3. Now estimate an AR (1) model for the log return r.
i. Test for the presence of ARCH effects in the residuals of this regression.
ii. Select and estimate an appropriate GARCH (p,q) model for the conditional variance of the
residuals of this regression. Justify your choice of selected model.
iii. Now extend your selected model in (ii) to include EGARCH and TGARCH effects.
Interpret the estimated EGARCH and TGARCH coefficients.OR
Discuss and explain any two from the following list:
(a) Long-run and short-run and ECM models
(b) Spurious regressions, cointegration, Engle-Granger and dynamic modelling approaches
(c) The limitations of using a single equation approach to testing for cointegration when there are
more than two variables in the model. Short-run dynamic models
Please provide a word count for this question AND ensure that your answer does not exceed
1000 words. Marks will be deducted for overlong essays.
4. Estimate a VAR (p) model for the log of four selected series.
i. Test for Granger causality between pair of these series (take log of these series first).
ii. Test for cointegration between four series (chosen in part 4) using Johansen method.
RUBRIC
QUALITY OF RESPONSE NO RESPONSE POOR / UNSATISFACTORY SATISFACTORY GOOD EXCELLENT Content (worth a maximum of 50% of the total points) Zero points: Student failed to submit the final paper. 20 points out of 50: The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. 30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. 40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. 50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. Use of Sources (worth a maximum of 20% of the total points). 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