Semi-parametric forecasts of the implied volatility surface using regression trees |
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Authors: | Francesco Audrino Dominik Colangelo |
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Affiliation: | 1.Fachbereich für Mathematik und Statistik,University of St. Gallen,St. Gallen,Switzerland;2.Swiss Finance Institute,Università della Svizzera italiana, USI,Lugano,Switzerland |
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Abstract: | We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive 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 boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model. |
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