Risk Everywhere: Modeling and Managing Volatility
By Tim Bollerslev, Benjamin Hood, J. Huss, Lasse Heje Pedersen. 2018
317 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/8816171c0acfe3cf2027ca3101c6032818026b6d
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Citations in the corpus
- Volatility‐Managed Portfolios (2017, methodology): “23 See also related work by Bollerslev et al. (2016) and Tang and Whitelaw (2011).”
- The Impact of Volatility Targeting (2018, unknown): “Adding up the squared overnight return leads to slightly less persistence (not reported), which is consistent with the findings of Bollerslev et al. (2018), who reported greater persistence of intraday volatility.”
- Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models (2019, methodology): “The augmented HAR-spec model reads As last model we apply the recently introduced centralized HAR model by Bollerslev et al. (2018).”