# Value at Risk - Ch.9: Forecasting Risk Correlations

#1

Reading Source: Textbook - Value at Risk

Key Takeaways:

• Time Varying Risk or Outliers?
• Modelling Time Varying Risk
• Moving Averages
• GARCH Estimation
• Long-Horizon Forecasts
• The RiskMetrics Approach
• Modeling Correlations
• Moving Averages
• GARCH
• Exponential Averages
• Crashes and Correlations
• Using Options Data
• Implied Volatilities
• ISDs as Risk Forecasts
• Conclusions
• Appendix 9.A Multivariate GARCH Models
• VEC(1,1) Model - Vector Model
• Diagonal VEC (DVEC) Model
• Scalar Model
• BEKK Model
• Factor Model
• Constant Conditional Correlation Model (CCC)
• Dynamic Conditional Correlation Model (DCC)

#2

This reading is quite technical, and Iâ€™m having trouble following some of the math/formulas.

How much depth do you think we need to know regarding the different models? For example, should I be memorizing the formula for the GARCH(1,1) process?

Thanks!

#3

Based on what I have seen they typically give the formula, but they would ask questions about the GARCH process like whether successive estimates are independent, the unconditional variance (alpha_0/(1-alpha_1-beta), the difference between GARCH and MA, things like that. There are a few old exam questions on the topic.

Value at Risk - Ch.9: Forecasting Risk and Correlations