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Updated A.Y. 2021-2022


1. Introduction
    Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes (white noise). Random walks and martingales.
    Review of prediction theory. Optimal prediction. Forecasting with nonstationary models: exponential smoothing.

2.  Volatility measurement and analysis
    Autoregressive Conditional Heteroscedasticity (ARCH) models: specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean.
    Generalized ARCH models, Integrated GARCH, Exponential GARCH models, GJR GARCH.

3. Review of multivariate distribution theory. Multivariate GARCH models. VEC and BEKK. Conditional correlation models: CCC, DCC. Factor models: Factor GARCH, O-GARCH. High-dimensional covariance estimation.

4. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5. Realized volatility. Market microstructure noise. Long memory.

6. Risk measurement: Value at Risk and expected shortfall. Copulae and tail dependence.

Textbook references

    Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.
    Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.
    Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.
    Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
    McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.
    Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
    Tsay, R.S. (2010). Analysis of Financial Time Series, Third Edition. Wiley.