Doctoral thesis

Semi-parametric implied volatility surface models and forecasts based on a regression tree-boosting algorithm


173 p

Thèse de doctorat: Università della Svizzera italiana, 2009 (jury note: summa cum laude)

English A new methodology for semi-parametric modelling of implied volatility surfaces is presented. This methodology is dependent upon the development of a feasible estimating strategy in a statistical learning framework. 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 predicting power of existing models, a grid is included in the region of interest and a cross-validation strategy is implemented 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 provides empirical evidence of the strong predictive power of the model. Accurate IVS forecasts also for single equity options assist in obtaining reliable trading signals for very profitable pure option trading strategies.
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