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

Francesco Audrino and Dominik Colangelo ()

University of St. Gallen Department of Economics working paper series 2009 from Department of Economics, University of St. Gallen

Abstract: 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.

Keywords: Option Trading Strategies; Implied Volatility Surface; Option Pricing; Forecasting; Boosting; Regression Trees (search for similar items in EconPapers)
JEL-codes: C13 C14 C53 C63 G13 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2009-08
New Economics Papers: this item is included in nep-cbe, nep-exp, nep-gth, nep-mic and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://ux-tauri.unisg.ch/RePEc/usg/dp2009/DP-0924-Au.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2009:2009-24

Access Statistics for this paper

More papers in University of St. Gallen Department of Economics working paper series 2009 from Department of Economics, University of St. Gallen Contact information at EDIRC.
Bibliographic data for series maintained by Martina Flockerzi ().