Financial Econometrics and Empirical Finance II

MSc. Finance/CLEFIN 2011/2012 Edition

Financial Econometrics and Empirical Finance II Professors Carlo Favero and Massimo Guidolin

COURSE OUTLINE/OBJECTIVES The course introduces the student to modern techniques in the area of financial (empirical) econometrics; in particular, the interaction between theory and empirical analysis is emphasised. The course also focused on the main econometric ingredient for portfolio allocation: forecasting the distribution of returns. Because of the applied emphasis, specific lab classes will illustrate how MATLAB programmes are constructed to practically implement the tools discussed in the lectures. The course takes as a prerequisite the introduction to MATLAB offered in the first semester within the MATLAB project at the Department of Finance (see the section Resources on the Dept. of Finance website for more details). Draft MATLAB codes for the solution of exercises are made available in advance on the course webpage, students are expected to work on them and refine during the laboratory sessions and in class. Relevant prerequisites are Financial Econometrics and Empirical Finance I (20191), Quantitative Methods for Finance (20188) and Theory of Finance (20135). You should always remember what Prof. Corielli has taught you and, in particular, what you CANNOT ignore about Probability and Statistics.

ASSESSMENT METHODS (THE EXAM) There will be one midterm/intermediate test (on April 18, 2012) and one final exam. Each of the two tests will carry a weight of 50%. A student can access the final exam starting from the midterm grade only if the midterm grade is 18 or higher. The formats of the midterm and final exams are the same: exams are open book, open notes. Questions will cover three different but highly interrelated topics: the econometric methods; the Matlab codes and coding structures introduced during the course; the data used and the exercises performed on these data during the lab sessions. The exam is not computer based, although there might be questions on MATLAB programming

TEXTBOOKS AND OTHER SUPPORT MATERIALS The material covered in the course is outlined in lecture notes made available via the class website. Lecture notes and class presentations of the material should be taken as a guidance for further study on the two main textbooks of the course: Brooks C. (2002) Introductory Econometrics for Finance, Cambridge University Press (chapters 1-9, henceforth “Brooks) Christoffersen P. F. (2012) Elements of Financial Risk Management, Academic Press 2nd edition (chapters 1-10, henceforth “Christoffersen”) For each topic we will also provide suggestions for further reading, whose consultation is left to the students’ initiative. To run the Matlab codes you need to download Lesage Spatial Econometrics. Here is a useful link: http://www.spatial-econometrics.com/ Additional packages to be downloaded and installed to correctly run the Matlab codes assigned/developed during the classes will be indicated during the lab sessions.

DETAILED SYLLABUS (required readings are indicated by a *)

1. The Econometrics of Financial Returns: an Introduction [C. Favero] *Lecture Notes. *CHRISTOFFERSEN, chapter 1. BROOKS, chapters 1-2. Bond, T. (2009) “The lost decade”, Barclays Capital. Cochrane, J. (1999) “New Facts in Finance” Federal Reserve Bank of Chicago, Economic Perspectives, Quarter 3. Fan, J. (2004) “An Introduction to Financial Econometrics”, mimeo, Princeton University.

2. The Dynamic Dividend Growth Model [C. Favero]. *Lecture Notes. Boudoukh, J., Michaely, R., Richardson, M. and M. Roberts (2004) “On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing”, NBER Working Paper 10651 (appeared in Journal of Finance in 2007). Campbell, J. Y., and R., Shiller (1988) “Stock Prices, Earnings, and Expected Dividends”, Journal of Finance, 43, 661-676. Campbell, J. Y., and R., Shiller (1988) “The Dividend-Price Ratio and Expectations of future Dividends and Discount Factors”, Review of Financial Studies, 1, 195-228. Campbell, J. Y., and R., Shiller (1998) “Valuation Ratios and The Long-Run Stock Market Outlook”, Journal of Portfolio Management, Winter, 11-28. Campbell, J. Y., and R., Shiller (2001) “Valuation Ratios and The Long-Run Stock Market Outlook: An Update”, Cowles Foundation discussion paper No. 1295. Cochrane J. (2006) “The dog that did Not Bark: a Defense of Return Predictability”, NBER Working Paper 12026 (appeared in Review of Financial Studies in 2008). Lettau, M., and S., Van Nieuwerburgh (2008), “Reconciling the Return Predictability Evidence”, Review of Financial Studies, 21,1607-1652. Shiller, R. (1981) “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?” American Economic Review, 71 421-436.

3. An Introduction to Univariate Time Series Analysis [C. Favero]. *Lecture Notes. *BROOKS, chapter 5. *CHRISTOFFERSEN, chapter 3.

4. An Introduction to Multivariate Time Series Analysis [C. Favero]. *Lecture Notes. *BROOKS, chapters 6-7. *CHRISTOFFERSEN, chapter 3. Campbell, J. Y. and R. Shiller (1987) "Cointegration and Present Value Models", Journal of Political Economy, 95, 1062-1088. Asness, C.(2002) “Fight the Fed model: the Relationship Between Future Returns and Stock and Bond Market Yields”, mimeo (appeared in Journal of Portfolio Management in 2003). Campbell, J. Y., and T., Vuolteenaho (2003), "Inflation Illusion and Stock Prices", NBER Working Paper 10263 (appeared in American Economic Review in 2004). Lander J., A., Orphanides and M., Douvogiannis (1997) "Earning Forecasts and the Predictability of Stock Returns", FED Board working paper, mimeo (appeared in Journal of Portfolio Management in 1997). Lettau, M., and S., Ludvigson (2005) “Expected Returns and Expected Dividend Growth”, Journal of Financial Economics, 76, 583-626.

5. The Econometrics of Stock Market Predictability [C. Favero]. *Lecture Notes. Boudoukh, J., M., Richardson, and R., Whitelaw (2008), “The Myth of Long-Horizon Predictability”, Review of Financial Studies, 21, 1577-1605. Campbell, J. Y., and S., Thomson (2005) “Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?”, NBER Working Paper 11468 (appeared in Review of Financial Studies in 2008). Valkanov, R. (2003) “Long-Horizon Regressions: Theoretical Results and Applications”, Journal of Financial Economics, 68, 201-232. Welch, I., and A., Goyal, (2008) “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction”, Review of Financial Studies, 21, 1455-1508.

6. Univariate Volatility Modeling: ARCH and GARCH [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapter 4. *ANDERSEN T., BOLLERSLEV T., CHRISTOFFERSEN P., DIEBOLD, F. (2006) “Volatility and Correlation Forecasting”, in Elliott G., Granger C., and Timmermann A. (eds.), Handbook of Economic Forecasting, Elsevier. Engle, R. F. (2001) “GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics”, Journal of Economic Perspectives, 15, 157-168. Hansen P.R., and A., Lunde (2005) “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?” Journal of Applied Econometrics, 20, 873–889.

7. Non-Normal Distributions and their Uses in GARCH Modeling [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapter 6. BROOKS, chapter 8. Jaschke, S. (2002), “The Cornish-Fisher-Expansion in the Context of Delta-Gamma-Normal Approximations”, Journal of Risk, Number 4, Summer 2002. McNeil A., (1998), “Calculating Quantile Risk Measures for Financial Return Series using Extreme Value Theory”, working paper. Teräsvirta T. (2009) “An Introduction to Univariate GARCH Models”, in Andersen, T., Davis, R., Kreiß, J.-P., and Mikosch, T., Handbook of Financial Time Series, Springer.

8. Brief Overview of Realized Volatility and Covariances [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapter 5. BROOKS, chapter 8. Andersen T. and Benzoni L. (2009) “Realized Volatility”, in Andersen, T., Davis, R., Kreiß, J.-P., and Mikosch, T., Handbook of Financial Time Series, Springer. McAleer, M., and M., Medeiros (2008), “Realized Volatility: A Review”, Econometric Reviews, 27, 10–45.

9. Markov and Regime Switching Models [M. Guidolin]. *Lecture Notes. *GUIDOLIN M. (2012) “Markov Switching Models in Empirical Finance”, in Advances in Econometrics (D. Drukker et al., editors), Emerald Publishers Ltd. *HAMILTON, J. (2005) “Regime Switching Models”, in New Palgrave Dictionary of Economics. BROOKS, chapter 9. Derman, E. (1999) “Regimes of Volatility Some Observations on the Variation of S&P 500 Implied Volatilities”, Goldman Sachs Quantitative Strategies Research Notes. Guidolin M., and A., Timmermann (2006) “An Econometric Model of Nonlinear Dynamics in the Joint Distribution of Stock and Bond Returns”, Journal of Applied Econometrics, 21, 1-22. Guidolin M., S., Hyde, D., MacMillan, and S., Ono, S. (2009) “Non-Linear Predictability in Stock and Bond Returns: When and Where Is It Exploitable?”, International Journal of Forecasting, 2009, 25, 373-399. Guidolin, M., and F., Ria (2011), “Regime Shifts in Mean-Variance Efficient Frontiers: Some International Evidence”, Journal of Asset Management, 12, 322-349. Lange T. and A., Rabhek (2009) “An Introduction to Regime Switching Models”, in Andersen, T., Davis, R., Kreiß, J.-P., and Mikosch, T., Handbook of Financial Time Series, Springer. Turner, C., R., Startz, and C., Nelson (1989) “A Markov Model of Heteroskedasticity, Risk and Learning in the Stock Market”, Journal of Financial Economics, 25, 3-22.

10. Multivariate Volatility and Correlation Modeling [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapters 7-9. *SILVENNOINEN,A., and T., TERÄSVIRTA (2009) “Multivariate GARCH Models”, in Andersen, T., Davis, R., Kreiß, J.-P., and Mikosch, T., Handbook of Financial Time Series, Springer. Andersen T., T., Bollerslev T., and F., Diebold F. (2009) “Parametric and Nonparametric Volatility Measurement”, in in Ait-Sahalia, Y., and Hansen, L., P., (eds.), Handbook of Financial Econometrics, Elsevier. Bauwens, L., S., Laurent, and J. Rombouts (2006) “Multivariate GARCH Models: A Survey”, Journal of Applied Econometrics, 21, 79-109, 2006. Litterman R., and K., Winkelmann (1998). “Estimating Covariance Matrices”, Goldman Sachs Quantitative Strategies Research Notes. Patton A., and K., Sheppard (2009) “Evaluating Volatility and Correlation Forecasts”, in Andersen, Davis, Kreiß, and Mikosch, Handbook of Financial Time Series, Springer.

11. Overview of Simulation-Based Methods [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapter 2. Barone-Adesi, G., K., Giannopoulos, and L., Vosper (1999) “VaR without Correlations for Nonlinear Portfolios”, Journal of Futures Markets, 19, 583-602. Christoffersen, P., F. Diebold, and T., Schuermann (1998) “Horizon Problems and Extreme Events in Financial Risk Management”, Federal Reserve Bank of New York Federal Reserve, Economic Policy Review, 109-118.

12. Applications of Financial Econometrics to Option Pricing [M. Guidolin]. *Lecture Notes. *CHRISTOFFERSEN, chapter 10. Garcia R., E., Ghysels, and E., Renault (2009) “The Econometrics of Option Pricing”, in Ait- Sahalia, Y., and Hansen, L., P., (eds.), Handbook of Financial Econometrics, Elsevier. Guidolin, M., and S., Goncalves (2006) “Predictable Dynamics in the S&P 500 Index Options Implied Volatility Surface”, Journal of Business, 79, 1591-1636. Heston, S., and S., Nandi (2000) “A Closed-Form GARCH Option Pricing Model”, Review of Financial Studies, 13, 585-626.