University of St.Gallen
research platform alexandria
search publications
browse publications
by person
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
 
by year

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

Francesco Audrino & Dominik Colangelo

fulltext etc. no fulltext attached
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.
   
type journal paper
   
keywords Implied Volatility, Implied Volatility Surface, Option Pricing, Forecasting,
Tree Boosting, Regression Tree, Functional Gradient Descent
   
language English
kind of paper journal article
date of appearance 20-9-2010
journal Statistics and Computing
publisher Springer Science (Dordrecht)
ISSN 0960-3174
ISSN (online) 1573-1375
DOI 10.1007/s11222-009-9134-y
volume of journal 20
number of issue 4
page(s) 421-434
review blind review
   
citation Audrino, F., & Colangelo, D. (2010). Semi-parametric forecasts of the implied volatility surface using regression trees. Statistics and Computing, 20(4), 421-434, DOI:10.1007/s11222-009-9134-y.