Risk Everywhere: Modeling and Managing Volatility

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 R’s 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 commonalities in the volatilities. We present a framework to evaluate the utility gains from the use of risk models, highlighting the interplay between transaction costs, the speed of different risk models, and their practical implementation.