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Program

EN IT

Updated A.Y. 2022-2023

PROGRAMME

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.