■ Modeling the Volatility of the FTSE100 Index Using High-Frequency Data Sets
In this chapter we refer to realized volatility as a consistent estimator of the squared root of the integrated variance and model the volatility of the FTSE100 index using high-frequency data sets in a period including the onset of the subprime mortgage debacle in the United States. We show that the presence of high and time-varying volatility of volatility is a fundamental stylized fact of stock market volatility, bringing additional uncertainty in the tails of the distribution of asset returns, explaining why events of several standard deviations may be observed, and rendering point forecasts of realized volatility a very poor measure of risk during critical moments of the financial crisis. We argue that higher moments of returns should be modeled to deal with this problem and show that the volatility of volatility is subject to strong leverage effects and is strongly and positively related to the level of volatility. In this chapter, we give a brief introduction on how this can be done within a realized volatility framework and explain how the daily distribution of returns (from which value-at-risk, expected shortfall, and other measures of interest can be extracted) can be forecasted from the model.