期刊名称：Discussion Papers of the Department of Economics, University of St.Gallen = Diskussionspapiere der Volkswirtschaftlichen Abteilung der Universität St.Gallen
出版年度：2007
卷号：2007
出版社：Universität St. Gallen
摘要：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.
