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Semi-parametric forecasts of the implied volatility surface using regression trees

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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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Correspondence to Francesco Audrino.

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Audrino, F., Colangelo, D. Semi-parametric forecasts of the implied volatility surface using regression trees. Stat Comput 20, 421–434 (2010). https://doi.org/10.1007/s11222-009-9134-y

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  • DOI: https://doi.org/10.1007/s11222-009-9134-y

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