Updated A.Y. 2018-2019
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.
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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.
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Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.
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