Toggle topbar
北京大学国家发展研究院

教学项目

sidenav header background
sidenav background

金融计量——2015年春季学期双学位课程介绍

发布日期：2015-03-03 09:41    来源：北京大学国家发展研究院

Financial Econometrics: Time Series Models        Spring 2015

NSD, PKU

 

Course Requirements:

A midterm (40%), three homework (20%) and a term paper (40%).

Useful textbook

Brook, Chris (2008): Introductory Econometrics for Finance. Cambridge University Press. Geanger, C. and P. Newbold (1977): Forecasting economic time series. Academic Press (New York)

Software knowledge: STATA or EVIEW

 

Course Outline

 

PART 0 Review: Principle of Maximum likelihood.

Likelihood function Large sample theory of MLE Some examples.

 

PART I: Modeling the Mean Equation

Basic time series models Trend, Seasonality and Stationary fluctuations White noise and Autocorrelations; Weak Stationatiry. AR(p), MA(q) and ARMA(p,q); stationarity and invertibility. Integrated Time series and Unit root tests I(1) Series, Unit root process ADF and PP Tests for a unit root. Nonstationarity due to Break

  C.  Time Series Regression

  1 Conditional mean and OLS

  2 OLS, t and F statistics

  3 Traps in Nonstationary time series regression: Spurious Regression vs cointegration regression.

  4 Diagnostics: Serial correlation and DW statistics

  5 A Useful linear model: ADL regression

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.

§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.

§Breen, W., L.Glosten and R. Jagannathan (1989): “Economic Significance of Predictable Variations in Stock Index Returns,” Journal of Finance XLIV, 1177-1189.

 

PART II  Modeling Volatility-GARCH Models

1 Autoregressive Conditional Heteroskedasticity/ARCH

2 Generalized ARCH/GARCH

3 Integrated GARCH and Break

4 Extensions: Asymmetric GARCH, EGARCH

References:

§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.

§Whitelaw, R. (1994): “Time Variations and Covariations in the Expectation and Volatility of Stock Market Returns,” Journal of Finance XLIX, 515-541.

§Christopher G. Lamoureux and William D. Lastrapes (1990): “Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,” Journal of Finance 45, 221-229.

 

PART III Multivariate Models

A  Multivariate Regression:

1. Seemingly Unrelated Regression /SUR

2 SEM and its Reduced form

3 VAR: Stationarity condition, Estimation and Causality

4 VAR: Impulse Response.

5 Cointegration and ECM.

B  Multivariate GARCH

1. VEC and DVEC

2. BEKK and DBEKK

3. Dynamic Conditional Correlation (DCC)

References:

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.


朗润

国家发展研究院官方微信

国家发展研究院官方微信
点击关注点击关注

相关链接

Copyright© 1994-2012 北京大学 国家发展研究院 版权所有, 京ICP备05065075号-1
保留所有权利，不经允许请勿挪用