VRM 3. Measuring and Monitoring Volatility

Learning Objectives

1) Explain how asset return distributions tend to deviate from the normal distribution.

2) Explain reasons for fat tails in a return distribution and describe their implications.

3) Distinguish between conditional and unconditional distributions, and describe the implications of regime switching on quantifying volatility.

4) Compare and contrast different parametric and non-parametric approaches for estimating conditional volatility.

5) Apply the exponentially weighted moving average (EWMA) approach to estimate volatility, and describe alternative approaches to weighting historical return data.

6) Apply the GARCH (1,1) model to estimate volatility.

7) Explain and apply approaches to estimate long horizon volatility/VaR and describe the process of mean reversion according to a GARCH (1,1) model.

8) Evaluate implied volatility as a predictor of future volatility and its shortcomings.

9) Describe an example of updating correlation estimates.