An introduction to realized volatility

Abstract

Financial market volatility is central to the theory and practice of asset pricing, option pricing, asset allocation, portfolio selection, portfolio rebalancing and various risk management applications. Although financial models often assume volatilities of securities to be constant, it is widely recognised that they vary over time. For example, in financial return series, the occurrence of volatility clustering is a well-known phenomenon. This recognition has spurred research into the distributional and dynamic properties of stock market volatility. Daily volatility is often measured from the returns over a historical window of a number (e.g. 30) of days. This has the disadvantage of always lagging current volatility. The literature on the estimation of current volatility has developed rapidly in recent times, but most of what we have learned from it is based on the estimation of parametric (G)ARCH or stochastic volatility models and on the analysis of implied volatilities from options. However, the validity of such volatility measures generally depends upon specific distributional assumptions, and in the case of implied volatilities, further assumptions concerning the market price of volatility risk