Option strategies based on semi-parametric implied volatility surface prediction

Item Type Journal paper
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.

Authors Audrino, Francesco & Colangelo, Dominik
Journal or Publication Title Journal of Investment Strategies
Language English
Subjects economics
HSG Classification contribution to scientific community
Refereed Yes
Date December 2011
Publisher Incisive Media
Place of Publication London
Volume 1
Number 1
Page Range 3-41
Number of Pages 39
ISSN 1460-1559
Depositing User Prof. Ph.D Francesco Audrino
Date Deposited 14 Nov 2011 16:19
Last Modified 23 Aug 2016 11:11
URI: https://www.alexandria.unisg.ch/publications/207130

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Citation

Audrino, Francesco & Colangelo, Dominik (2011) Option strategies based on semi-parametric implied volatility surface prediction. Journal of Investment Strategies, 1 (1). 3-41. ISSN 1460-1559

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https://www.alexandria.unisg.ch/id/eprint/207130
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