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Option strategies based on semi-parametric implied volatility surface prediction

Francesco Audrino & Dominik Colangelo

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abstract We investigate whether a more sophisticated technique able to forecast accurately the future movements of the implied volatility surface may help in improving the performance of basic option strategies. To this goal we construct a set of strategies using predicted option returns for a forecasting period of ten trading days and form profitable hold-to-expiration, equally weighted, zero-cost portfolios with one month at-the-money options. The accurate predictions of the implied volatility surface dynamics are obtained using a statistical machine learning procedure based on regression trees. These forecasts assist in obtaining reliable option returns used as trading signals in our strategies. We test the performance of the proposed strategies on options on the S\&P100 and on its constituents between 2002 and 2006 getting positive annualized returns of up to more than 50\%. Comparing such performance to the ones obtained without using any complex model for the implied volatility surface we show that in most cases differences are small.
   
type journal paper
   
keywords
   
language English
kind of paper journal article
date of appearance 12-2011
journal Journal of Investment Strategies
publisher Incisive Media (London)
ISSN 1460-1559
volume of journal 1
number of issue 1
page(s) 3-41
review double-blind review
   
citation Audrino, F., & Colangelo, D. (2011). Option strategies based on semi-parametric implied volatility surface prediction. Journal of Investment Strategies, 1(1), 3-41.