COURSE NAME: Financial Econometrics
COURSE LEVEL: PhD Course
LECTURER: ZHU Jie
教育背景：
1997年，上海交通大学管理学院，本科
2004年，丹麦奥胡斯大学经济与管理学院，经济学硕士
2008年，丹麦奥胡斯大学经济与管理学院，经济学博士
工作背景：
1997年-2002年，上海市经济委员会
2007-2009年，丹麦奥胡斯大学
2009-2010年，丹麦南丹麦大学
科研成果：
Zhu, J., 2009, Testing for Expected Return and Market Price of Risk in  
Chinese A-B Share Market – A Geometric Brownian Motion and  
Multivariate GARCH Model, Mathematics and Computers in Simulation 79,  
pp. 2633-2653

Zhu, J., 2009, Pricing Volatility of Stock Returns with Volatile and  
Persistent Components, Financial Markets and Portfolio Management 23,  
3, pp. 243-269

Christensen, B. J., Nielsen, M. Ø. and Zhu, J., Long Memory in Stock  
Market Volatility and the Volatility-in-Mean Effect: The FIEGARCH-M  
Model, Journal of Empirical Finance (Forthcoming).

Zhu, J., 2008, FIEGARCH-M and International Crises: A Cross-Country  
Analysis, CREATES RP 2008-16, Aarhus University (Working paper)

COURSE DESCRIPTION:
The course is concerned with the theory and practice of financial economics, which is a highly empirical discipline. The objective of the course is to provide some methods to obtain inference for financial economics by introducing econometrical and statistical tools useful for conducting analysis on various topics in this field. The topics start with the measurement of returns (which is random in general), followed with market efficiency, event studies, equity valuation, fixed-income securities analysis, derivative pricing, financial risk management, and others. Since most financial data are in time series, the econometrical models dealing with time series analysis are also introduced in the course. The analysis considers basic AR, MA and ARMA models, extending to nonlinear models, high-frequency analysis and market microstructure, and others. Some advanced treatments in this field are also covered, which include the MCMC method and Kalman Filter analysis. Finally, some time is given on programming skills.

LEARNING OBJECTIVES:
After following the course, students should be able to
·
Explain different methods for predicting stock returns from their own history.
·
Know how the microstructure of stock markets affects the short-run behavior of returns.
·
Know how to derive CAPM and test its validity.
·
Know how to determine multiple factors affecting stock returns via the statistical and fundamental analysis approach.
·
Formulate and implement parametric option pricing models.
·
Pricing path-dependent derivatives via Monte Carlo simulation.
·
Know the term-structure models of interest rate and how to pricing fixed-income securities.
·
Know the AR, MA, and ARMA models in time series.
·
Apply GARCH and other nonlinearity models in finance.
·
Implement the econometrical analysis in computer software (SAS, OxMetrics, etc.)
TEACHING METHOD: Lectures and discussion of cases
LITERATURE:

The Econometrics of Financial Markets, 1997, by John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay, Princeton University Press.

Analysis of Financial Time Series, 2005, by Ruey S. Tsay, John Wiley & Sons, Inc.

Lecture Notes and other related articles