Value at Risk - Ch.9: Forecasting Risk Correlations



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)


This reading is quite technical, and I’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?



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