ECONOMETRICS OF FINANCIAL MARKETS. MSc. in Quantitative Finance. Educational Aim

Transcription

1 Giovanni Urga Professor of Finance and Econometrics Faculty of Finance Cass Business School 106 Bunhill Row London EC1Y 8TZ Tel: +44 (0) Fax: +44 (0) Director, Centre for Econometric Analysis ECONOMETRICS OF FINANCIAL MARKETS Professor Giovanni Urga Faculty of Finance Cass Business School MSc. in Quantitative Finance Term 1: - Term 2: January-March, 2012 Lectures: - Wednesday, 09:00-12:00 Room LG003 Office Hours: - Tuesdays, (or by appointment) Room g.urga@city.ac.uk (for updatings) Educational Aim The course kicks-off with a description of some stylised facts and offers a short introduction to the main characteristics of high-frequency data. An extended presentation of the econometric techniques developed to model financial time series, such Maximum Likelihood, GIVE and GMM estimators, follows. Linear and nonlinear models provide a unified framework to study specific returns distributions, treatment of seasonality, and intraday and intraweek volatility. Univariate and multivariate (G)ARCH are introduced to model time varying variances and covariances. Fractional properties of financial time series are evaluated via the modelling of credit spreads. We introduce methods to forecast risk and returns, and dynamic markets correlation analysis, essential tools to understand interactions between financial markets, i.e. term structure and the bond markets, the foreign exchange market and the stock price volatility. We conclude with the presentation of realized volatility, a recently proposed non-parametric estimate of the return variation. 1

2 Education Objectives Provide a detailed knowledge of the tools of financial econometrics. Illustrate the techniques with actual examples of applied works using high frequency data The course will provide the participants with: Knowledge of how econometrics can be applied to get useful insights about financial-world behaviour. 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, and the attendance of Foundations of Econometrics in Term 1. Some reading and a crash review will be provided at the beginning of the course. Course Requirements Students are expected to attend a 3-hour weekly lecture, which also contains practical implementations of the theoretical material covered during the lectures, using the econometric packages PcGive-13 and G@RCH-6. Assessment There will be one assessed (individual) coursework and a final test (individual) in April/May 2010 that will count as 25% and 75% respectively. The coursework s length must not exceed four A4 sheets (double sided), it must be word-processed and a hard copy to be submitted to the course office (and electronically via Moodle) by 4pm of Friday, 23 th of March Note: 1. If two or more courseworks are identical will be graded zero. 2. Extensions are given only for exceptional circumstances and by the course director. Lectures The lectures will embody activities such as formal lectures and participative discussions on research papers of relevance to the course. The list of the topics to be covered during the lectures is provided below. A more detailed outline of the issues covered is provided at the beginning of each lecture. 2

3 Course Outline Week 1 (25/01/12). Preliminaries: high frequency finance and data types - Some notes on financial econometrics; - High frequency data and methodology of high frequency research; - Markets and data types (spot, futures and option markets); - Markets: foreign exchange, over-the counter interest rate, interest rate futures, bond futures, commodity futures and equity markets; - Introduction to PcGive and G@RCH. Readings: Bollerslev (2001)*; Engle (2001)*. Further Readings: Advanced Information Nobel (2003), Taylor (2008, Chapters 1-2) or Mills-Markellos (2008, Chapter 2); Diebold (2004), Tsay (2005: Chapter 1); Decorogna et al. (2001, Chapters 1-3); Tauchen, G. (2001), Valdez (2007). Week 2 (1/02/12). Linear time series models and forecasting - AR, MA and ARMA models; - Specification strategies, forecasting returns with linear models; - Decomposing time series, measures of persistence and trends. - Non stationarity, cointegration and spurious regression. Readings: Richards (1995)*; Sueppel (2005)*. Further Readings: Diebold (2004); Hendry (2004); Mills-Markellos (2008, Chapter 2); Tsay (2005: Chapters 2); Decorogna et al. (2001: Chapters 3). Week 3 (8/02/12). GMM and maximum likelihood estimation methods in finance. Readings: Buse (1982)*. Bera and Bilias (2002)*. Further Readings: Journal of Business and Economic Statistics (2002); Matyas, L. (1999); Hall (2005); Cochrane (2005). Week 4 (15/02/12). Advanced Information Nobel Prize 2003 and Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity. - Empirical Macroeconomics Readings: Advanced Information Nobel Prize (2003*, 2011*). Week 5 (22/02/12): Modelling volatility: univariate (G)ARCH models. Seasonality. - Volatility patterns and markets volatility; - Univariate (G)ARCH models; - Seasonal components; - Intraday and intraweek seasonalities. Readings: Laurent (2009); Teräsvirta (2009)*. Further Readings: Bollerslev (2009); Andersen et al. (1999); Bera and Higgings (1993); Mills-Markellos (2008, Chapter 5); Decorogna et al. (2001: Chapters 6 and 7); Zivot (2009). Week 6 (29/02/12). Modelling volatility: MGARCH models. - Intraday volatility and multivariate (G)ARCH models; 3

4 - Modelling heterogeneity volatility and forecasting short-term volatility; - Practical applications using G@RCH. Readings: Laurent (2009); Silvennoinen and Terasvirta (2009)*. Further Readings: Bauwens, Laurent and Rombouts (2006); Clements (2005); Decorogna et al. (2001); Tsay (2005). Andersen et al (2005). Week 7 (07/03/12). Dynamic conditional correlations (DCC) models, multivariate risk and contagion. - Estimating the dependence of financial time series; - Correlation behaviour of high data frequencies; - Stability of return correlation; - Estimating and testing cross-markets correlations with normal and asymmetric multivariate Laplace distributions for the innovations. - Risk management analysis and measuring contagion. Readings: Engle (2002)*. Further Readings: Cappiello, Engle and Sheppard (2006 or 2004WP), Decorogna et al. (2001): Chapter 10; Tsay (2005: Chapters 8 and 10), Urga, Cajigas and Ghalanos (2011). Week 8 (14/03/12). Realized volatility. Forecasting risk and returns. - Forecasting volatility and volatility for Value-at-Risk; - Forecasting returns over multiple time horizons; - Measuring forecast quality/accuracy; - Practical implementations using G@RCH. Reading: Laurent (2009); Andersen and Benzoni (2008a)*. Further Readings: Andersen et al. (2003, 2004); Clements (2005); Decorogna et al. (2001). Week 9 (21/03/12). The impact of macro-announcements on the term structure, foreign exchange rates and asset prices. Readings: Flannery, M. J. and A.A. Protopapadakis (2002)*. Week 10 (28/03/12). Fractional integration and long memory processes in finance: a short introduction. Revision class * Papers in the lecture notes package. The list of topics given above though provisional is intended to be a fairly accurate guide to the sorts of topics that we shall aim to cover in the course, even though I may add some topics to the list and drop some others. Reading List The material covered during the lectures will be drawn from textbooks and papers listed below. Relevant reading (compulsory) material and lecture notes prepared by the lecturer will be distributed at the beginning of the course. 4

5 Textbooks Andersen, T.G., Davis, R.A., KreiB, J.P., Mikosch, T. (2009), HANDBOOK OF FINANCIAL TIME SERIES, Springer-Verlag. Dacorogna, M.M., R. Gencay, U. Muller, R.B. Olsen and O.V. Pictet (2001), AN INTRODUCTION TO HIGH FREQUENCY FINANCE, Academic Press. Laurent, S. (2009), ESTIMATION AND FORECASTING ARCH MODELS USING 6, Timberlake Consultants Ltd. Mills, T. C. and R. N. Markellos (2008), THE ECONOMETRIC MODELLING OF FINANCIAL TIME SERIES, 3 rd Edition, Cambridge University Press. Taylor, S. (2008), MODELLING FINANCIAL TIME SERIES, World Scierntific Publishing Co. Pte. Ltd. Tsay, S. R. (2005), ANALYSIS OF FINANCIAL TIME SERIES, 2 nd Edition, Wiley. Papers Aas, K. and X.K. Dimakos (2004), Statistical modelling of financial time series: an Introduction, Norwegian Computing Center, SAMBA/08/2004. Advanced Information Nobel Prize (2003), Robert F. Engle and Clive W. J. Granger. Advanced Information Nobel Prize (2011), Thomas J. Sargent and Christopher A. Sims Andersen, T. G. And L. Benzoni (2008a), Realized volatility, Chapter in Handbook of Financial Time Series, Springer Verlag. Andersen, T. G. And L. Benzoni (2008b), Stochastic volatility, Chapter in Encyclopaedia of Complexity and System Science, Springer Verlag. Andersen, T. G., Bollerslev, T., Diebold, F. X. and P. Labys (1999), (Understanding, optimizing, using and forecasting) realized volatility and correlation, Manuscript, Northwester University, Duke University and Pennsylvania University. Published in revised form as Great realizations in Risk, March 2000, Andersen, T., Bollerslev, T., Diebold, F.X. and Ebens, H. (2001), "The distribution of stock return volatility" Journal of Financial Economics, 61, Andersen, T. G., Bollerslev, T., Diebold, F. X. and P. Labys (2003), Modelling and forecasting realized volatility, Econometrica, 71, Andersen, T. G., Bollerslev, T., Diebold, F. X. and J. Wu (2004), Realized beta: persistence and predictability, Manuscript, Northwester University, Duke University and Pennsylvania University. 5

6 Andersen, T. G., Bollerslev, T., Christoffersen, P. F. and Diebold, F. X. (2005), Practical volatility and correlation modeling for financial market risk management, in M. Carey and R. Stulz (eds), Risks of Financial Institutions, University of Chicago Press for NBER (forthcoming). Barone Adesi, G., Gagliardini, P. And G. Urga (2004), Testing asset pricing models with coskewness, Journal of Business and Economics Statistics, 22, Bauwens, L., Laurent, S. and J.V.K. Rombouts (2006), Multivariate GARCH models: a survey, Journal of Applied Econometrics, 21, Bera, A. K. and Y. Bilias (2002), "The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis", Journal of Econometrics, 107, Bera, A.K. and M. L. Higgins (1993), "ARCH models: properties, estimation and testing", Journal of Economic Surveys, 7, Bollerslev, T. (2001), Financial econometrics: past developments and future challenges, Journal of Econometrics, 100, Bollerslev, T. (2009), Glossary of ARCH (GARCH), in Volatility and Time Series: Essays in Honour of Robert F. Engle (eds. Tim Bollerslev, Jeffrey R. Russell and Mark Watson), Oxford University Press, Oxford, UK. Buse, A. (1982), "The likelihood ratio, Wald, and Lagrange multiplier tests: an expository note", American Statistician, 36, Campagna, C. (2002), Dynamic conditional correlation: empirical applications to Italian and European stock markets, mimeo, University of Bergamo, Italy. Cappiello, L., Engle, R. and K. Sheppard (2006), "Asymmetric dynamics in the correlations of global equity and bond returns", Journal of Financial Econometrics, 4, Castle, J.L., Doornik, J.A., and D.F. Hendry (2011), Evaluating Automatic Model Selection, Journal of Time Series Econometrics, Vol. 3: Iss. 1, Article 8. Diebold, F. X. (2004), The Nobel memorial prize for Robert F. Engle, Scandinavian Journal of Economics, 106, Dittmar, R.F. (2002), Nonlinear pricing kernels, kurtosis preference, and evidence from the cross section of equity returns, Journal of Finance, LVII, Engle, R. (2001), Financial econometrics A new discipline with new methods, Journal of Econometrics, 100, Engle, R. (2002), "Dynamic conditional correlation - A simple class of multivariate GARCH models", Journal of Business and Economic Statistics, 20(3),

7 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, 1-10 Flannery, M. J. and A.A. Protopapadakis (2002), Macroeconomic factors do aggregate stock returns, The Review of Financial Studies, 15, influence Forbes, K.J. and R. Rigobon (2002), "No contagion, only interdependence: measuring stock market comovements", Journal of Finance, 57, Harvey,C.R. and A. Siddique (2000), Conditional skewness in asset pricing tests, Journal of Finance, LV, Hendry, D. F. (2004), "The Nobel memorial prize for Clive W. J. Granger", Scandinavian Journal of Economics, 106, Richards, A.J. (1995), "Comovements in national stock market returns: evidence of predictability, but not cointegration", Journal of Monetary Economics, 36, Silvennoinen, A. and Terasvirta, T. (2009), Autoregressive Conditional Heteroskedasticity Models: Multivariate, in (Andersen et al., eds) Handbook of Financial Time Series, pp Teräsvirta, T. (2009), Autoregressive conditional heteroskedasticity models: univariate, in (Andersen et al., eds) Handbook of Financial Time Series, pp Urga, G., Cajigas, J.P., Ghalanos, A. (2011), Dynamic Conditional Correlation Models with Asymmetric Multivariate Laplace Innovations, Centre for Econometric Analysis, Cass Business School. Zivot, E. (2009), Practical issues in the analysis of univariate GARCH models, in (Andersen et al., eds) Handbook of Financial Time Series, pp Papers Further Readings: Banerjee, A. and G. Urga (2005), Modelling structural breaks, long memory and stock market volatility: an overview, Journal of Econometrics, 129, Bollerslev, T., Engle, R.F. and D.B. Nelson (1994), ARCH models, Chapter 49, in R.F. Engle and D.L. McFadden (eds), HANDBOOK OF ECONOMETRICS, Vol. 4, Elsevier Science. Journal of Business and Economic Statistics (2002), 20 th Anniversary Issue on the Generalized Method of Moments, Vol. 20, N. 4, October. Pagan, A. (1996), The econometrics of financial markets, Journal of Empirical Finance, 3,

8 Tauchen, G. (2001), Notes on financial econometrics, Journal of Econometrics, 100, Textbooks: Campbell, J. Y., A.W. Lo and A.C. MacKinlay (1997), THE ECONOMETRICS OF FINANCIAL MARKETS, Princeton University Press. Clements, M. P. (2005), EVALUATING ECONOMETRIC FORECASTS OF ECONOMIC AND FINANCIAL VARIABLES, Palgrave Texts in Econometrics. Dunis, C. (eds) (1996), FORECASTING FINANCIAL MARKETS, Wiley. Dunis, C. and B. Zhou (eds) (1998) NONLINEAR MODELLING OF HIGH FREQUENCY FINANCIAL TIME SERIES, Wiley. Franses, P.H. and D. van Dijk (2000), NON-LINEAR TIME SERIES MODELS IN FINANCE, Cambridge University Press. Gencay, R., F. Selcuk and B. Whitcher (2002), AN INTRODUCTION TO WAVELETS AND OTHER FILTERING METHODS IN FINANCE AND ECONOMICS, Academic Press. Gourieroux, C. and J. Jasiak (2001), FINANCIAL ECONOMETRICS, Princeton University Press. Hall, A. (2005), GMM ESTIMATION METHODS, Oxford University Press. Matyas, L. (1999) (ed), GENERALISED METHOD OF MOMENTS ESTIMATION, Cambridge University Press. McNeil, A.J., R. Frey, and P. Embrechts (2005), QUANTITATIVE RISK MABAGEMENT. CONCEPTS, TECHNIQUES AND TOOLS, Princeton University Press. Mills, T. C. (1999), THE ECONOMETRIC MODELLING OF FINANCIAL TIME SERIES, Cambridge University Press. Rachev, S.T., S. Mittnik, F. J. Fabozzi, S.M. Focardi, T. Jasic (2007), FINANCIAL ECONOMETRICS. FROM BASICS TO ADVANCED MODELING TECHNIQUES. Wiley Finance. Valdez, S. (2007), AN INTRODUCTION TO GLOBAL FINANCIAL MARKETS, 5 th Edition, Palgrave MacMillan. Tuesday, 10 January