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Risk Everywhere: Modeling and Managing Volatility

Tim Bollerslev, Benjamin Hood, John Huss and Lasse Heje Pedersen

No 12687, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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: high-frequency data; Market and volatility risk; realized utility; realized volatility; risk modeling and forecasting; risk targeting; volatility trading (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mst, nep-rmg and nep-upt
Date: 2018-02
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