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Forecasting Variance Swap Payoffs, Jonathan Dark, Xin Gao, Thijs Van Der Heijden, Federico Nardari
Forecasting Variance Swap Payoffs, Jonathan Dark, Xin Gao, Thijs Van Der Heijden, Federico Nardari
WCBT Faculty Publications
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 …
Kalman Filter Vs Alternative Modeling Techniques And Applied Investment Strategies, Heather E. Dempsey
Kalman Filter Vs Alternative Modeling Techniques And Applied Investment Strategies, Heather E. Dempsey
Doctoral Dissertations (DBA)
This thesis examines the efficacy of alternative modeling techniques to predict stock market returns modeled with time-varying coefficients with the goal of developing and implementing a trading strategy that yields excess returns. First, we determine the modeling technique with the smallest forecast error using historical predictors: the differenced dividend-price ratio, lagged S&P 500 returns, and the change in implied volatility. The candidate modeling techniques include both constant and recursive ordinary least squares (OLS) regression methods and diverges from previous return forecast literature with the comparison of a state-space model (SSM) cast as a VAR(1) process to each OLS technique. The …