Risk Everywhere: Modeling and Managing Volatility
54 Pages Posted: 28 Jan 2016 Last revised: 22 Mar 2017
Date Written: March 21, 2017
Based on a unique high-frequency dataset for more than fifty commodities, currencies, equity indices, and fixed income instruments spanning more than two decades, we document strong similarities in realized volatilities patterns across assets and asset classes. Exploiting these similarities within and across asset classes in panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and more conventional procedures that do not incorporate the information in the high-frequency intraday data and/or the similarities in the volatilities. A utility-based framework designed to evaluate the economic gains from risk modeling highlights the interplay between parsimony of model specification, transaction costs, and speed of trading in the practical implementation of the different risk models.
Keywords: Market and volatility risk, high-frequency data, realized volatility, risk modeling and forecasting, volatility trading, risk targeting, realized utility
JEL Classification: C22, C51, C53, C58
Suggested Citation: Suggested Citation