Financial Econometrics 2025/2026

1 Introduction. Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes. Random walks and martingales. Review of prediction theory. Optimal prediction.
(Lectures 1-3)

2 Volatility measurement and analysis: autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean. Generalized ARCH models, Integrated GARCH, Exponential GARCH models. GJR-GARCH, Leverage. Fat and heavy tails.
(Lectures 4-7)

3 Multivariate GARCH models. VEC and BEKK. Conditional correlation models: constant and dynamic, CCC, DCC. Factor models: Factor GARCH, O-GARCH. Large dimensional covariance and correlation matrices. (Lectures 8-10)

4. Copulae and tail dependence (Lecture 11)

5. Risk Measurement: Value at Risk and Expected Shortfall. (Lectures 12-14).

6. Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter. (Lectures 15-16)

7. Realized volatility. Long memory. (Lectures 17-18).

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Fonte: GOMP