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Option trading strategies based on semi-parametric implied volatility surface prediction

We propose constructing a set of trading strategies using predicted option returns for a relatively small forecasting period of ten trading days to form profitable hold-to-expiration, equally weighted, zero-cost portfolios based on 1-month at-the-money call and put options. We use a statistical machine learning procedure based on regression trees to accurately predict future implied volatility surfaces. Such accurate forecasts are needed to obtain reliable option returns used as trading signals in our strategies. We test the performance of the proposed strategies on options on the S&P 100 and on its constituents for the time period between 2002 and 2006: positive annualized returns of up to more than 50% are achieved.