Financial Econometrics Spring,2011
Financial Econometrics : Time Series Models Spring 2011
HSBC Business School, Peking University
Class meeting time: every Thu 3:30-6:30pm & Fri 1:30-4:30pm, from Feb 24 to April 1st.
Instructor: Professor CSJ Chu
This course covers fundamental time series models frequently applied in finance. Specifically, time series models and time series regression. Perquisites are basic statistics and probability. Course grade is based on various homework, a midterm and a final exam.
Useful textbook- Brook, Chris (2008): Introductory Econometrics for Finance. Cambridge University Press.
Course Outline
PART I: Review of Statistics and Probability
A. Random variable and CDF
B. Some useful distributions
C. Random vector: Covariance/Independence
D. Parametric Statistical Inference
1. Point Estimation: Methods of finding a point estimator
2. Sampling distribution: CLT
3. Interval Estimation
4. Hypothesis Testing
PART II Univariate Time Series Models
A. Weakly stationary time series
1 White noise
2 Autoregressive process
3 Moving average process
4 ARMA(p.q) model
B. Volatility: GARCH Models
1 Autoregressive Conditional Heteroskedasticity/ARCH
2 Generalized ARCH/GARCH
3 Extensions: Asymmetric GARCH, EGARCH
C. Nonstationary Time series
1 Martingale
2 Integrated time series/Unit root
3 ADF test
References:
§Poterba, J. and L. Summers (1988): “Mean Reversion in Stock Prices: Evidence and Implications,” Journal of Financial Economics 22, 27-50.
§Hasbrouck, J. and T. Ho (1987): “Order Arrival, Quote Behavior and the Return-Generating Process,” The Journal of Finance 42, 1035-1048.
§Hasbrouck, J. and T. Ho (1987): “Order Arrival, Quote Behavior and the Return-Generating Process,” The Journal of Finance 42, 1035-1048.
§Akgiray,V. (1989): “Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts,” The Journal of Business 62, 55-80.
§Berkowitz, J. and J. O'Brien(2002): “ How Accurate Are Value-at-Risk Models at Commercial Banks?” The Journal of Finance 57, 1093-1111.
PART III Time Series Regression
A Single equation model
1 Conditional mean and OLS
2 OLS, t and F statistics
3 Dummy variable and nonlinear explanatory variable
4 GLS
B Diagnostics
1 Serial correlation and DW statsitcs
2 Spurious Regression
3 Parameter shift
C Useful linear model: ADL regression
Reference:
§Balvers,R., T. Cosimano and B. McDonald, (1990): “Predicting Stock Returns in an Efficient Market”, Journal of Finance XLV, 1109-1127.
§Fama, E and K. French (1989): “Business Conditions and Expected Returns on Stock and Bonds”, Journal of Financial Economics 25, 23-49.
§Whitelaw, R.(1994): “Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns,” Journal of Finance XLIX, 515-541.
§Breen,W., L.Glosten and R. Jagannathan(1989): “Economic Significance of Predictable Variations in Stock Index Returns,” Journal of Finance XLIV, 1177-1189.
PART IV Multivariate Models
A Multivariate Regression: Seemingly Unrelated Regression /SUR
1 Reduced form
2 SUR Estimation/SURE
B Multivariate Time Series: Vector Autoregression
1 Stationarity condition
2 Estimation
3 Causality
4 Cointegration and ECM.
Reference:
Sundaram Janakiramanan, Asjeet S. Lamba(1998): “An empirical examination of linkages between Pacific-Basin stock markets,” Journal of International Financial Markets,
Institutions and Money 8, 155–173.
Gong-meng Chen, Michael Firth and Oliver Meng Rui (2002): “Stock market linkages: Evidence from Latin America,” Journal of Banking & Finance 26, 1113–1141.