Portsmouth Business School
Economics and Finance Subject Group
Financial Econometrics (Part 2)
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.
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.
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 Wiley
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)
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) to the Assessment Office, Richmond Building. Essential workings/results/tables can be included in the appendix. Please also submit all your Eviews workings, data and the assignment on to a CD/disk 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) from the DataStream software. 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.
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)).
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?
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?
Are your conclusions about stationary/non-stationary of e and r confirmed by appropriate unit root tests?
Estimate and select an appropriate ARMA (p,q) model for e. In selecting your preferred model, explain how you use the information provided by:
The estimated coefficients (and their t-statistics);
Ljung-Box Q-statistics for autocorrelation in the residuals; and
AIC and SBC information criteria for choosing between alternative models.
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.
Carry out forecast (out of sample) of e for 100 observations based on your chosen model and comment on your results.
Now estimate an AR (1) model for the log return r.
Test for the presence of ARCH effects in the residuals of this regression.
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.
Now extend your selected model in (ii) to include EGARCH and TGARCH effects. Interpret the estimated EGARCH and TGARCH coefficients.
Discuss and explain any two from the following list:
Long-run and short-run and ECM models
Spurious regressions, cointegration, Engle-Granger and dynamic modelling approaches
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.
Estimate a VAR (p) model for the log of four selected series.
Test for Granger causality between pair of these series (take log of these series first).
Test for cointegration between four series (chosen in part 4) using Johansen method.
Professor Shabbar Jaffry
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