Economics 512 Financial Econometrics

AMATH 546/ECON 589: Financial Econometrics and Quantitative Risk Management
Home Syllabus Homework Notes Announcements R Hints	Syllabus Spring 2013 Last updated: June 5, 2013 Note: "QRM" denotes Quantitative Risk Management; "FRF denotes Financial Risk Forecasting; "FMUND" denotes Financial Modeling Under Non-Gaussian Distributions; "SDAFE" denotes Statistics and Data Analysis for Financial Engineering.
Week 1: 4/1 and 4/3	Course Introduction, Properties of asset returns and Risk Measures
Textbook Readings	Lecture Notes and Additional Readings
FRF chapters 1 and 4 QRM chapter 1; chapter 4, sections 1-2; chapter 2 sections 1-2. FMUND chapter 1, chapter 2 and chapter 8 SDAFE chapter 2, chapter 4 and chapter 19	Class slides for overview of quantitative risk management (updated April 1, 2013). Class slides for return definitions and properties of returns (updated April 1, 2013). Class slides for risk measures (updated April 5, 2012). Powerpoint slides for distribution properties of returns (updated April 3, 2013). R code used in powerpoint examples (updated April 3, 2013).
Week 2: 4/8 and 4/10	Asset and Portfolio Risk Measures: Properties, Computation and Estimation
Textbook Readings	Lecture Notes, Additional Readings and Paper Discussions
FRF chapter 4; chapter 5, sections 2 and 3. QRM chapter 2, section 3. FMUND chapter 8 and chapter 5, sections 2-5. SDAFE chapter 5 and chapter 19	Class slides for risk budgeting (updated April 8, 2013). Class slides for risk budgeting and risk reporting (updated April 10, 2013) Powerpoint slides for risk report examples (updated April 10, 2013) Class slides for estimating risk measures (updated April 10, 2013) Powerpoint slides of R examples (updated April 23, 2012) R code used in powerpoint slides (updated April 23, 2012) Acerbi, C. and Tasche, D. (2001). "Expected Shortfall: a natural coherent alternative to Value at Risk". Abaxbank Working Paper. Bertsimas, D., Lauprete, G.J., and Samarov, A. (2004). "Shortfall as a risk measure: properties,optimization and applications," Journal of Economic Dynamics and Control. Christoffersen, P. (2009). “Value-at-Risk Models,” in T.G. Andersen et al (eds.) Handbook of Financial Time Series, Springer. Gourieroux, C., J.P. Laurent, and O. Scaillet (2000). “Sensitivity Analysis of Values at Risk”, Journal of Empirical Finance. Qian, E.E. (2006). “On the Financial Interpretation of Risk Contribution: Risk Budgets Do Add Up”, Journal of Investment Management. Dan´ıelsson, J., Jorgensen, B.N., Samorodnitsky, G., Sarma, M. and de Vries, C. (2005). "Subadditivity Re–Examined: the Case for Value–at–Risk," Working Paper, London School of Economics. Boudt, K., B. Peterson and C. Croux (2008). "Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns," Journal of Risk. Epperlein, E. and A. Smillie (2006). “Cracking VAR with Kernels,” Risk. Yamai, Y. and T. Yoshiba (2002). “Comparative Analyses of Expected Shortfall and Value-at-Risk: Their Estimation Error, Decomposition, and Optimization,” Institute for Monetary and Economic Studies, Bank of Japan. Maillard, D. (2012). "A User's Guide to the Cornish Fisher Expansion," SSRN Working paper 1997178.
Week 3: 4/15 and 4/17	Volatility Modeling
Textbook Readings	Lecture Notes, Additional Readings and Paper Discussions
FRF chapter 2; chapter 5, sections 5-6. QRM chapter 2, section 3; chapter 4, sections 1-4. FMUND chapter 4, sections 1-6. SDAFE chapter 18	Class slides for univariate GARCH lectures (updated 4/17/2013). Powerpoint slides for univariate GARCH lectures (updated 4/17/2013). R code used for powerpoint examples (updated April 17, 2013) Ghalanos, A. (2013). "Introduction to the rugarch package". Francq, C., and Zakoian, J.-M. (2008). "A Tour in the Asymptotic Theory of GARCH Estimation," Handbook of Financial Time Series. Zivot, E. (2008). "Practical Issues in the Analysis of Univariate GARCH Models," Handbook of Financial Time Series. Splus script for examples in paper. practicalGarch.ssc Diebold, F.X. and J. Lopez (1995). "Modeling Volatility Dynamics," NBER Technical Working Paper No. 173. Engle, R.F. (2001). "GARCH 101: The Use of ARCH/GARCH Model in Applied Economics," Journal of Economic Perspectives, 15(4), 157-168. Hansen, Peter R., and Lunde, A. (2005). "A FORECAST COMPARISON OF VOLATILITY MODELS: DOES ANYTHING BEAT A GARCH(1,1)?", Journal of Applied Econometrics. Granger, C. and S.-H. Poon (2003). "Forecasting Financial Market Volatility," Journal of Economic Literature, Vol. 41, No. 2. Kuester, K., Mittnick, S., and Paollela, M.S. (2006). "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, 4(1), 53-89. PhD Section Presentation Papers: Goldberg, L, Hayes, M.Y., Menchero, J. and Mitra, I. (2009). "Extreme Risk Management", SSRN Working Paper No. 1341363. Goldberg, L, Hayes, M.Y., Menchero, J. and Mitra, I. (2009). "Extreme Risk Analysis", MSCI Barra Research Insights, SSRN Working Paper No. 1404820.
Week 4: 4/22 and 4/24	Advanced Volatility Modeling and Backtesting Risk Models
Textbook Readings	Lecture Notes and Additional Readings
FRF chapter 8 QRM chapter 2, section 3.5; chapter 4, section 4.3. FMUND chapter 8, section 6 SDAFE chapter 19	Class slides on advanced volatility modeling (updated April 29, 2013). Powerpoint slides for advanced GARCH lectures. Class slides on backtesting risk models (updated May 3, 2012). Powerpoint slides for backtesting risk models (updated May 3, 2012). R code for advanced volatility modeling. R code for backtesting risk models (updated May 3, 2012). Kuester, K., Mittnick, S., and Paollela, M.S. (2006). "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, 4(1), 53-89. Andersen, T., and Bollerslev, T. (1998). "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts". International Economic Review, 39(4), 885-905. Angelidis, T. and Degiannakis S. (2007) "Backtesting VaR Models: An Expected Shortfall Approach" Working Paper, Dept. of Economics, University of Crete. PhD Section Presentation Papers: Creal, D., Koopman, S.J., and Lucas, A. (2011). "Generalized Autoregressive Score Models with Applications," Working Paper, University of Chicago, Booth School of Business. Creal, D., Koopman, S.J., and Lucas, A. (2012). "Univariate Generalized Autoregressive Score Volatility Models," Working Paper, University of Chicago, Booth School of Business. Creal, D., Koopman, S.J., and Lucas, A. (2012). "Generalized Autoregressive Score Models," AENORM, Vol. 20(75).
Weeks 5, 6 and 7: 5/6, 5/8, 5/13 and 5/15	Liquidity Risk, Multivariate Models: Dynamic Covariance and Correlation Modeling
Textbook Readings	Lecture Notes and Additional Readings
FRF chapter 3. QRM chapter 3, sections 1-3; chapter 4, sections 5 and 6 FMUND chapter 6. SDAFE chapter 7	Midterm Exam - Wednesday May 1st. Class slides for multivariate GARCH lectures (updated 5/15/2013). Powerpoint slides for multivariate GARCH lectures based on S-PLUS (updated 5/15/2013). Powerpoint slides for multivariate GARCH based on R (updated May 15, 2013) R code for multivariate GARCH estimation (updated May 15, 2013) Engle, R.F. (2001). "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditinoal Heteroskedasticity Models," Journal of Business and Economic Statistics, 20, 339-350. Ding, Z., and Engle, R.F. (2001). "Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing," unpublished manuscript, Department of Economics, UC San Diego. Silvennoinen, A., and Terasvirta, T. (2008). "Multivariate GARCH Models," Handbook of Financial Time Series. PhD Section Presentation Papers: Engle, R.F. (2012). "Dynamic Conditional Beta"
Week 8: 5/20 and 5/22
Textbook Readings	Lecture Notes and Additional Readings
FRF chapter 1, section 8 QRM chapter 5 FMUND chapter 6. SDAFE chapter 8.	Class slides on copulas (updated May 15, 2013). Powerpoint examples for copula examples in R (updated May 20, 2013) R code for copula examples. Class slides on factor model risk analysis (updated May 29, 2013). Powerpoint slides for factor model risk analysis (updated May 29, 2013). R code for factor model risk analysis. Yan, J. (2007). "Enjoy the Joy of Copulas: With a Package copula". Journal of Statistical Software Kojadinovic, I., and Yan, J. (2010). "Modeling Multivariate Distributions with Cointinuous Margins Using the copula R Package." Journal of Statistical Software. Meucci, A. (2007). “Risk Contributions from Generic User-Defined Factors”, Risk. Goodworth, T. and C. Jones (2007). “Factor-based, Non-parametric Risk Measurement Framework for Hedge Funds and Fund-of-Funds,” The European Journal of Finance. Jiang, Y. (2009). Overcoming Data Challenges in Fund-of-Funds Portfolio Management, PhD Thesis, Department of Statistics, University of Washington.
Weeks 9 and 10: 5/29, 6/3 and 6/5	Factor Models and Factor Models Risk Decompositions
Textbook Readings	Lecture Notes and Additional Readings
QRM chapter 3, section 4. SDAFE chapter 17	Class slides on estimating macroeconomic factor models (updated May 29, 2013) Class slides on estimating fundamental factor models (updated June 5, 2013). Class slides on estimating statistical factor models (updated June 5, 2013) Powerpoint slides for factor model estimation in R (updated June 5, 2013). Class slides on bond and option risk analysis (updated June 3, 2013) R code for estimation of factor models (updated May 22, 2012) R code for factor model risk analysis. berndtInvest.csv Bender, J. and F. Nielsen (2010). "The Fundamentals of Fundamental Factor Models", MSCI Research Insight.
Final Exam. Due Friday 6/14/13 at 5 pm. Brownless and Engle (2011). "Volatility, Correlation, and Tails for Systemic Risk Management"

AMATH 546/ECON 589: Financial Econometrics and Quantitative Risk Management

Syllabus