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FIN 657 Syllabus Financial Econometrics for Risk Modeling

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FIN 657 Syllabus Financial Econometrics for Risk Modeling FIN 657 Syllabus Financial Econometrics for Risk Modeling Lubin School of Business Pace University FALL 2015 Instructor: Elena Goldman Email: [email protected] Office: 1 Pace Plaza, W488 Phone: 212-618-6516 Course CRN: 72814 Course Location & Time: W408 (GPACT Room), Thursdays 5:40-8:30pm Office Hours: Tuesdays 3:30-6:00pm and Thursdays 3:00-5:30pm Course Description: This course teaches financial econometrics with emphasis on estimation and forecast- ing of time series models in finance. Students will learn how to measure and forecast financial volatility and correlations and become proficient with GARCH type models and historical volatilities. These methods will be used to measure risk and analyze alternative approaches to calculating Value at Risk, dynamic portfolio selection and risk control. The course will also examine implied volatilities from options, vari- ance swaps, credit risk models, market (in)efficiency, dynamic relationships between global financial markets and high frequency volatility. The course will teach estima- tion, Monte Carlo simulations and programming methods. Course Objectives: The objective of this course is to teach methods used in risk modeling and make students competitive in risk management job market. The tools learned in the course will be useful for Risk Management class and for the FRM exam. Prerequisites: Foundations of Finance (MBA 648 Managerial Finance) and a familiarity with simple 1 probability and statistics including least squares regression (MBA 646 Data Analysis for Decision Making). Software: There will be substantial use of the R and EViews software. R/R-Studio is an open source software. Eviews is available in the computer lab in W408 GPACT Room. Students can also get their own copy of Eviews at a discounted price. Grading: The course will have four learning exercises, project, final exam and in-class quick quizzes. These quizzes will take about 5 minutes at the beginning of class and cover previous lecture. There are no make-ups but the lowest will be dropped. Learning Exercises (40%) Quick Quizzes (lowest score dropped) (10%) Project (25%) Final Exam (25%) Readings: Lecture materials, articles and cases posted on Blackboard. Brooks, Chris. Introductory Econometrics for Finance, 2014, 3rd edition, Cambridge University Press. Additional Texts: Tsay, Rue. An introduction to analysis of financial data with R, 12th edition, Wiley. Danielsson, J. Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with implementation in R and Matlab, 2011, Wiley. Koop, G. Analysis of Financial Data, 2006, Wiley. Georgakopoulos, H. Quantitative Trading with R, 2015, Palgrave Macmillan. James, G., Witten, D., Hastie, T., and R. Tibshirani. An Introduction to Statistical Learning with Applications in R, 2013, Springer. Project and Final Exam Dates: Project: Thursday, December 3 Final Exam: Thursday, December 10 Learning Exercises due dates will be posted on Blackboard. 2 Academic Integrity: Educational institutions should aspire to instill in their students an appreciation for and the practice of ethical conduct. All students are required to adhere to the statement of academic integrity outlined in the Pace University catalog. Academic integrity infractions can include, but are not limited to, copying and presenting the work of another as your own, collaborating with others on assignments intended to be done individually, using unauthorized resources such as an instructor’s manual to complete assignments, copying the work of others during an exam, and failing to reference the work of others or creating fake references in your assignments. You may receive a failing grade in any assignment, exam, or course in which an infraction takes place, and you may be suspended or expelled from the school. When in doubt about what might be considered an academic integrity infraction, the best course of action is to ask your instructor for clarification. Reasonable accommodations for students with disabilities: The University’s commitment to equal educational opportunities for students with disabilities includes providing reasonable accommodations for the needs of students with disabilities. To request an accommodation for a qualifying disability, a student must self-identify and register with the Coordinator of Disability Services for his or her campus. No one, including faculty, is authorized to evaluate the need and arrange for an accommodation except the Coordinator of Disability Services. Moreover, no one, including faculty, is authorized to contact the Coordinator of Disability Services on behalf of a student. For further information, please see Information for Students with Disabilities on the University’s web site. 3 Course Topics and Readings The following is a tentative list of lecture topics with corresponding readings. Module 1 (Weeks 1-3): Financial Returns and Time Series Volatility. Introduction to financial econometrics. Basic returns data characteristics, asymmetry and fat tails, examples of distributions, introduction to copulas. Financial Volatility - Causes, Consequences, and Global Patterns. Historical volatility and volatility estimator used by RISKMETRICS model, ARCH/GARCH Models with exten- sions. Brooks Ch. 1, 9.3-9.14; Tsay Ch. 1,4; Danielsson Ch. 1,2; Koop Chs. 2, 3,12 Learning Exercise 1 Module 2 (Weeks 3-4): Estimation of CAPM and Multifactor Models. Event Studies. Estimation, hypotheses testing and specification errors. Dummy variables - seasonality in financial data. Heteroscedasticity. Introduction to factor models and principal components analysis Harvard Business School Case Study "Multifactor Models" 9-207-056, 2007 Brooks Chs. 3, 4, 5, 10.2-10.4, 14.9-14.10; Tsay Ch. 5.3.1; Koop Chs 4, 5, 6,7 Learning Exercise 2 Module 3 (Weeks 5-6): Univariate and Multivariate time series modelling and forecast- ing. Autocorrelation, stationarity, unit root, tests of random walk (weak market efficiency), ARIMA models, forecasting with time series models. Multivariate time series models, modelling long run relationships in finance. Cointegra- tion, Granger causality, vector autoregression,relationship between international stock indices. Brooks Ch 6,7,8; Tsay Ch. 2, 3; Koop Ch 8,9,10,11 Learning Exercise 3 Module 4 (Weeks 7-8): Value at Risk, Downside Risk, Credit Risk, Logit/Probit Limitted Dependent Variables Models, Predicting bankruptcy from Financial Distress. Brooks Ch. 9.10-9.19, 13.8-13.9, Ch. 12; Tsay Ch. 7 ; Danielsson Ch.4,5 Learning Exercise 4 4 Module 5 (Weeks 9-12): Options Implied Volatility and Volatility trading. Correlation Models - Applications to Portfolio Choice and Systemic Risk mesures. Monte Carlo Simulations, Simulation methods for VaR, Backtesting and stress testing. Numerical methods, programming risk management problems. High Frequency Volatility and Trading. Brooks, Ch.5; Danielsson Ch.3, 6.2,7,8; Tsay Ch. 5.4 Derman, Emanuel (2004) "Trading Volatility as an Asset Class", Columbia University Module 6 (Weeks 12-14): Course Review. Project Presentation and Final Exam 5.
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