Forecasting Implied Volatility Surfaces

Francesco Audrino and Dominik Colagelo ()

University of St. Gallen Department of Economics working paper series 2007 from Department of Economics, University of St. Gallen

Abstract: We propose a new semi-parametric model for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree-boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the tree boosting. Back testing the out-of-sample appropriateness of our model on a large data set of implied volatilities on S&P 500 options, we provide empirical evidence of its strong predictive potential, as well as comparing it to other standard approaches in the literature.

Keywords: Implied Volatility; Implied Volatility Surface; Forecasting; Tree Boosting; Regression Tree; Functional Gradient Descent (search for similar items in EconPapers)
JEL-codes: C13 C14 C51 C53 C63 G12 G13 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2007-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2007:2007-42

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