Measuring and Modeling Risk Using High-Frequency Data
23 Pages Posted: 1 Nov 2008
Date Written: August 5, 2008
Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index.
Keywords: Realized Volatility, Realized Betas, Volatility Modeling
JEL Classification: C13, C14, C22, C52, C53
Suggested Citation: Suggested Citation