Linda Allen, Chapter 2: Quantifying Volatility in VaR Models is a 50-minute instructional video analyzing the following concepts:
* Explain how asset return distributions tend to deviate from the normal distribution.
* Explain reasons for fat tails in a return distribution and describe their implications.
* Distinguish between conditional and unconditional distributions.
* Describe the implications regime switching has on quantifying volatility.
* Explain the various approaches for estimating VaR.
* Compare and contrast parametric and non-parametric approaches for estimating conditional volatility.
* Calculate conditional volatility using parametric and non-parametric approaches.
* Explain the process of return aggregation in the context of volatility forecasting methods.
* Explain how implied volatility can be used to predict future volatility
* Explain long horizon volatility/VaR and the process of mean reversion according to an AR(1) model.
* Calculate conditional volatility with and without mean reversion.
* Describe the impact of mean reversion on long horizon conditional volatility estimation