ADVANCED FINANCIAL ECONOMETRICS MSc. in Mathematical Trading and Finance (Full-Time and Part-Time)

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1 Syllabus ADVANCED FINANCIAL ECONOMETRICS MSc. in Mathematical Trading and Finance (Full-Time and Part-Time) Lecturer: Giovanni Urga Professor of Finance and Econometrics Faculty of Finance, Cass Business School (UK) Director, Centre for Econometric Analysis & Professor of Econometrics Department of Economics, Universita di Bergamo (Italy) Term 1: September-December, 2011 Lectures: Tuesdays, (Room G001) Office Hours: Tuesdays ( for FT and 17:00-18:00 for PT) and by appointment for PT. Room Educational Aim The course will provide a review of the general linear model and an extended presentation of the techniques that have been developed in the past decade to model the main characteristics of financial time series. Both univariate and multivariate models will be considered. With the former we will cover topics such as autoregressive and moving average representations, stationary and non-stationary time series, conditional and unconditional forecasts, and the distinction between deterministic and stochastic trends. We will extend these ideas to a multivariate framework, focusing on structural economic models and VARs, cointegration analysis and equilibrium correction formulation of dynamic models. Simultaneous equation models will also be introduced. ARCH and GARCH models will be introduced to model time varying variances and covariances. Finally, we will introduce the GMM estimation method of wide use in finance, ix

2 CHAPTER 0. SYLLABUS especially in estimating and evaluating asset pricing models. Empirical exercises on term structure and the bond market, the foreign exchange market and the stock price volatility will accompany the presentation of theoretical models. Education Objectives Provide detailed knowledge of analytical tools of econometrics. Provide knowledge of how econometrics can be applied to get useful insights about real-world behaviour. Illustrate methods with actual examples of empirical finance. Familiarise with the techniques by studying empirical papers, and undertaking practical works which may be asked to most applied financial economists to model the main characteristics of financial time series. Prerequisites A good mathematical and statistical background. A crash review will be provided at the math and stats induction. Course Requirements Students are expected to attend 10 three-hour lectures which will include practical sessions using PcGive10 for financial applications. During the three tutorials students will also discuss relevant theoretical and applied papers provided by the lecturer. Assessment There will one assessed coursework (25% of the final grade) and a 3-hour examination (75%) in January The coursework has to be undertaken individually even though collaboration amongst students during their preparation is welcome. The coursework will imply the practical implementation of the theoretical material covered during the lectures using a data set provided by the lecturer. The deadline for the coursework to handed in the course office is Friday, 25th November 2011 by 4pm. The coursework s length must not exceed four A4 sheets, and it must be word-processed. If two or more courseworks are identical they will be graded zero. Extensions by the lecturer will be given only in exceptional circumstances, and under the previous approval of the course director. c Giovanni Urga, 2011 x Adv Fin Econometrics,

3 Lectures The lectures will embody activities such as formal lectures and participative discussions on research papers of relevance to the course. A list of the topics to be covered during the lectures is provided below, though a more detailed outline of the issues to be examined will be provided at the beginning of each lecture. Course Outline Chapter 1: The general linear regression model. Economic and econometric models. Least squares estimators (OLS): classical assumptions and properties. Restricted least-squares estimators. Goodness of fit and the analysis of variance. Hypothesis testing: Student s t-test, the F-test. DW statistic. Readings: Greene (2003), Chapters 1, 2, 3, 4, 6. Bollerslev (2001); Engle (2001); Chapter 2: The general linear regression model (continued). Departures from the classical assumptions: non-scalar covariance matrix and stochastic regressors. Generalized least squares (GLS), Seemingly Unrelated Regression (SURE), and Instrumental Variables (IV). Readings: Greene (2003), Chapters 5, 10, 11, 12. Chapter 3: Further topics: Interpretation of coefficients and alternative functional forms. Use of dummy variables. Criteria for model selection Econometric modelling using PcGive13. Readings: Greene (2003), Chapters 5, 7, 10. Doornik (2009). Doornik and Hendry (2009a-c). Chapter 4: Maximum likelihood estimation and asymptotic properties. Three test procedures asymptotically equivalent: likelihood ratio test (LR), the Wald test (W) and Lagrange Multiplier test (LM). Empirical applications: estimating linear factor models. Readings: Greene (2003), Chapter 17. Buse (1982). Further readings: Cochrane (2005); Verbeek (2008). c Giovanni Urga, 2011 xi Adv Fin Econometrics,

4 CHAPTER 0. SYLLABUS Chapter 5: Time series models and non-stationarity. AR, MA, ARMA and ARIMA modelling. Non stationary time series, unit roots and long memory processes. Models of non-stationary time series. Difference stationary series and stochastic trends. Trend stationary series and deterministic trends. Unit roots testing procedure: Dickey and Fuller and Augmented Dickey and Fuller tests. Empirical applications: testing for unit root/long memory in volatility of financial assets and term structure. Readings: Greene (2003), Chapter 19, 20; Holden and Perman (1994, pp ). Further readings: Relevant chapters in Enders (2004); Gourieroux and Jasiak (2001); Hendry (1995); Verbeek (2008). Chapter 6: Models with non-stationary variables. Spurious regression. Cointegration. and (Error)Equilibrium-correction models. Cointegration in single equations: Engle- Granger (OLS). Vector Autoregression (VAR) models. Cointegration systems: Johansen (Maximum Likelihood). Empirical applications: testing for comovements and contagion in stock markets. Readings: Greene (2003), Chapter 20. Advances Information Nobel Prize 2003 (Engle and Granger); Ferson et al (2003a,b); Holden and Perman (1994, pp ); Murray (1994); Richards (1995); Forbes and Rigobon (2002). Further readings: Relevant chapters in Enders (2004); Gourieroux and Jasiak (2001); Hendry (1995); Verbeek (2008). Chapter 7: Dynamic Econometric Modelling. Alternative econometric methodologies. Generalto-specific dynamic specification. Exogeneity and causality in econometrics. Econometric and financial modelling using Autometrics in PcGive 13. Empirical applications: the impact of news on Fed funds future contracts. Readings: Greene (2003), Chapter 8; Gilbert (1986). Further readings: Relevant chapters in Gourieroux and Jasiak (2001), Hendry (1995); Verbeek (2008). Chapter 8: Testing model validity: misspecification tests. Non-nested models, encompassing and model selection. Empirical applications: the money demand/consumption functions in UK. Readings: Greene (2003), Chapter 8. Gilbert (1986). Further readings: Relevant chapters in Gourieroux and Jasiak (2001), Hendry (1995); Doornik and Hendry (2009a); Verbeek (2008). c Giovanni Urga, 2011 xii Adv Fin Econometrics,

5 Chapter 9: Autoregressive conditional heteroscedasticity models: an extended introduction. Measuring volatility over time. ARCH, GARCH, ARCH-M, E-GARCH models. Lagrange multiplier test for ARCH. (Q)ML estimation method and forecasting. Introduction to multivariate GARCH models. Empirical applications: modelling asset pricing and foreign exchange rates using PcGive 13 and Readings: Greene (2003), Chapter 11. Teräsvirta, T. (2009) and Zivot (2009). Further readings: Relevant chapters Campbell, Lo, MacKinley (1997); Cochrane (2001); Enders (2004); Gourieroux and Jasiak (2001); Verbeek (2008). Chapter 10: GIVE and GMM estimators. GIVE and GMM estimation methods. Properties. Applications in finance. Empirical applications: estimating intertemporal asset pricing models, linear factor models and short rate processes. Readings: Greene (2003), Chapter 18; Bera and Bilias (2002). Further readings: Relevant chapters in Campbell, Lo, MacKinley (1997); Cochrane (2005, Chapters 10-16); Hall (2005); Verbeek (2008). The list of topics given above is provisional and is intended to be a fairly accurate guide to the sort of topics that we shall aim to cover in the course, even though some further topics may be added to the list and some others dropped. Reading List The lecture notes and the papers listed below will be provided at the beginning of the course. Though no single textbook covers the topics to be presented during the lectures, the main reference textbook is Greene (2003). You may wish to discuss alternative references with the lecturer. TEXTBOOKS: Campbell, J.Y., Lo, A. and A.C. MacKinley (1997), THE ECONOMET- RICS OF FINANCIAL MARKETS, Princeton University Press. Cochrane, J.H. (2005), ASSET PRICING, Princeton: Princeton University Press. Enders,W. (2004), APPLIED ECONOMETRIC TIME SERIES, 2ndEdition, John Wiley & Sons. Greene, W.H. (2003), ECONOMETRIC ANALYSIS, 5th Edition, Prentice Hall International. c Giovanni Urga, 2011 xiii Adv Fin Econometrics,

6 CHAPTER 0. SYLLABUS Hall, A. (2005), GENERALIZED METHODS OF MOMENTS, Oxford University Press. Hendry, D.F. (1995), DYNAMIC ECONOMETRICS, OxfordUniversity Press. Hendry, D. F. and B. Nielsen (2007), ECONOMETRIC MODELING: A LIKELIHOOD APPROACH, Princeton University Press. Rachev, S.T., S. Mittnik, F. J. Fabozzi, S.M. Focardi, T. Jasic (2007), FI- NANCIAL ECONOMETRICS. FROM BASICS TO ADVANCED MOD- ELING TECHNIQUES. Wiley Finance. Taylor, S. (2008), MODELLING FINANCIAL TIME SERIES, World Scientific Publishing Co. Pte. Ltd. Tsay, R. S. (2010), ANALYSIS OF FINANCIAL TIME SERIES, 3rd Edition. Wiley & Sons, New York. Verbeek, M. (2008), A GUIDE TO MODERN ECONOMETRICS, 3rd Edition, Wiley & Sons, New York. PAPERS: 1. Bollerslev, T. (2001), Financial econometrics: past developments and future challenges, Journal of Econometrics, 100, Engle, R. (2001), Financial econometrics A new discipline with new methods, Journal of Econometrics, 100, Buse, A. (1982), The likelihood ratio, Wald, and Lagrange multiplier tests: an expository note, American Statistician, 36, Holden, D. and R. Perman (1994), Unit roots and cointegration for economist, in B.B.Rao (ed), COINTEGRATION FOR THE APPLIED ECONOMIST, St.Martin s Press. 5. Advanced Information Nobel Prize 2003 (Engle and Granger). 6. Ferson, W.E., Sarkissian, S. and T. T. Simin (2003a), Spurious regression in financial economics?, The Journal of Finance, LVIII, Ferson, W.E., Sarkissian, S. and T. T. Simin (2003b), Is stock return predictability spurious?, Journal of Investment Management, 1, Murray, M. P. (1994), A drunk and her dog: an illustration of cointegration and error correction, The American Statistician, 48, Richards, A.J. (1995), Comovements in national stock market returns: evidence of predictability, but not cointegration, Journal of Monetary Economics, 36, c Giovanni Urga, 2011 xiv Adv Fin Econometrics,

7 10. Forbes, K.J. and R. Rigobon (2002), No contagion, only interdependence: measuring stock market comovements, Journal of Finance, 57, Gilbert, C.L. (1996), Professor Hendry s econometric methodology, Oxford Bulletin of Economics and Statistics, 48, Teräsvirta, T. (2009), Autoregressive conditional heteroskedasticity models: univariate, in (Andersen et al., eds) Handbook of Financial Time Series, pp Zivot, E. (2009), Practical issues in the analysis of univariate GARCH models, in (Andersen et al., eds) Handbook of Financial Time Series, pp Bera, A. K. and Y. Bilias (2002), "The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis", Journal of Econometrics, 107, OXMETRICS6andPCGIVE13MANUALS: Doornik, J.A. (2009). An Introduction to OxMetrics 6.1. London: Timberlake Consultants Press. Doornik, J.A. and Hendry, D.F. (2009a). Empirical Econometric Modelling using PcGive 13. Volume I. London: Timberlake Consultants Press. Doornik, J.A. and Hendry, D.F. (2009b). Modelling Dynamic Systems using PcGive 13. Volume II. London: Timberlake Consultants Press. Doornik, J.A. and Hendry, D.F. (2009c). Econometric Modelling using PcGive 13. Volume III. London: Timberlake Consultants Press. G@RCH MANUAL: Laurent, S. (2009). Estimation and Forecasting ARCH Models Using G@RCH 6. London: Timberlake Consultants Press OTHER TEXTBOOKS AVAILABLE: Amemiya, T. (1994), INTRODUCTION TO STATISTICS AND ECONO- METRICS, Harvard University Press, Cambridge, MA and London, U.K. Curthbertson, K., S. G. Hall and M.P. Taylor (1992), APPLIED ECONO- METRIC TECHNIQUES, Philip Allan. Davidson, R. and J. G. MacKinnon (1993), ESTIMATION AND INFER- ENCE IN ECONOMETRICS, Oxford University Press, Oxford. c Giovanni Urga, 2011 xv Adv Fin Econometrics,

8 CHAPTER 0. SYLLABUS Davidson, R. and J. G. MacKinnon (2004), ECONOMETRIC THEORY AND METHODS, Oxford University Press, Oxford. Favero, C.A. (2001), APPLIED MACROECONOMETRICS, Oxford University Press, Oxford. Goldberger, A. S. (1991), A COURSE IN ECONOMETRICS, Harvard University Press, Cambridge, MA. Gourieroux, C. and J. Jasiak (2001), FINANCIAL ECONOMETRICS, Princeton University Press. Hansen, B. (2010), ECONOMETRICS, Lecture Notes. Hamilton, J. D. (1994), TIME SERIES ANALYSIS, Princeton University Press. Hayashi, F. (2000), ECONOMETRICS, Princeton University Press, Princeton. Judge, G.G., W.E. Griffiths, R.C. Hill, H. Lutkepohl, T.-C. Lee (1995), THE THEORY AND PRACTICE OF ECONOMETRICS, Wiley & Sons, New York. Maddala, G. S. (2001), INTRODUCTION TO ECONOMETRICS, 3rd Edition, Wiley. McNeil, A.J., R. Frey, and P. Embrechts (2005), QUANTITATIVE RISK MANAGEMENT. CONCEPTS, TECHNIQUES AND TOOLS, Princeton University Press. Mills, T. C. (1999), THE ECONOMETRIC MODELLING OF FINAN- CIAL TIME SERIES, 2nd Edition, Cambridge University Press. Mills, T. C. and R. N. Markellos (2008), THE ECONOMETRIC MOD- ELLING OF FINANCIAL TIME SERIES, 3rd Edition, Cambridge University Press. Peracchi, F. (2001), ECONOMETRICS, Wiley & Sons, New York. Pindyck, R. S. and Rubinfeld, D. L. (1998), ECONOMETRIC MODELS AND ECONOMIC FORECASTS, McGraw-Hill International, New York. Rachev, S.T., S. Mittnik, F. J. Fabozzi, S.M. Focardi, T. Jasic (2007), FI- NANCIAL ECONOMETRICS. FROM BASICS TO ADVANCED MOD- ELING TECHNIQUES. Wiley Finance. Spanos, A. (1999), PROBABILITY THEORY AND STATISTICAL IN- FERENCE, Cambridge University Press. c Giovanni Urga, 2011 xvi Adv Fin Econometrics,