Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.
This paper studies variance risk premiums in the credit market. Using a novel data set of swaptions quotes on the CDX North America Investment Grade index, we find that returns of credit variance swaps are negative and economically large. Shorting variance swaps yields an annualized Sharpe ratio of almost six, eclipsing its counterpart in fixed income or equity markets. The returns remain highly statistically significant when accounting for transaction costs, cannot be explained by established risk-factors, and hold for various investment horizons. We also dissect the overall variance risk premium into payer and receiver variance risk premiums. We find that exposure to both parts is priced. However, the returns for payer variance, associated with bad economic states, are roughly twice as high in absolute terms.
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