Document Type
Peer-Reviewed Article
Publication Date
12-2022
Abstract
We investigate the predictability of payoffs from selling variance swaps on the S&P500, US 10-year treasuries, gold, and crude oil. In-sample analysis shows that structural breaks are an important feature when modeling payoffs, and hence the ex post variance risk premium. Out-of-sample tests, on the other hand, reveal that structural break models do not improve forecast performance relative to simpler linear (or state invariant) models. We show that a host of variables that had previously been shown to forecast excess returns for the four asset classes, contain predictive power for ex post realizations of the respective variance risk premia as well. We also find that models fit directly to payoffs perform as well or better than models that combine the current variance swap rate with a realized variance forecast. These novel findings have important implications for variance swap sellers, and investors seeking to include volatility as an asset in their portfolio.
DOI
10.1002/fut.22371
Recommended Citation
Dark, J., Gao, X., van der Heijden, T., & Nardari, F. (2022). Forecasting variance swap payoffs. Journal of Futures Markets, 42(12), 2135-2164. Doi:10.1002/fut.22371
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License. Open access publishing facilitated by The University of Melbourne, as part of the Wiley - The University of Melbourne agreement via the Council of Australian University Librarians.
First published online: 04 August 2022.