Topics
1. Introduction; Revision on covariance, correlation and law of iterated expectations
2. White noise and other innovation series; Putting the tools to use; Autoregressive (AR) processes, Moving average (MA) processes, ARMA processes
3. Auto-covariance functions; Predictability and R2; Leaning outcomes; Sample examination questions
4. Predictability of asset return volatility; Autoregressive heteroscedasticity (ARCH) processes
5. Stationarity, moments, and restriction on parameters; Maximum likelihood estimation of GARCH models
6. Testing for volatility clustering; Leaning outcomes; Sample examination questions
7. Extensions of the univariate ARCH model
8. Choosing a volatility model
9. Multivariate volatility models
10. Introduction; Evaluating forecasts
11. Comparing forecasts; Forecast encompassing and combining
12. Introduction; Historical simulation; Weighted historical simulation
13. Models based on normal distribution; Models based on flexible distributions; Semi-parametric models
14. The conditional autoregressive Value at Risk (VaR) model; Leaning outcomes; Sample examination questions
15. Evaluating VaR forecasts
16. Comparing and combining VaR forecasts. Leaning outcomes; Sample examination questions
17. Modeling seasonality/diurnality
18. Modeling seasonality/diurnality (continued); Modeling durations
19. Modeling durations (continued)