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Forecasting variance swap payoffs
Author(s) -
Dark Jonathan,
Gao Xin,
Heijden Thijs,
Nardari Federico
Publication year - 2022
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22371
Subject(s) - variance swap , realized variance , econometrics , variance (accounting) , volatility (finance) , predictability , predictive power , swap (finance) , portfolio , asset allocation , economics , variance decomposition of forecast errors , covariance , risk premium , variance risk premium , volatility swap , stochastic volatility , statistics , implied volatility , financial economics , mathematics , volatility risk premium , finance , philosophy , accounting , epistemology
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.