DP12687 Risk Everywhere: Modeling and Managing Volatility
|Author(s):||Tim Bollerslev, Benjamin Hood, John Huss, Lasse Heje Pedersen|
|Publication Date:||February 2018|
|Keyword(s):||high-frequency data, Market and volatility risk, realized utility, realized volatility, risk modeling and forecasting, risk targeting, volatility trading|
|JEL(s):||C22, C51, C53, C58|
|Programme Areas:||Financial Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=12687|
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.