Options
Semi-parametric forecasts of the implied volatility surface using regression trees
Journal
Statistics and Computing
ISSN
0960-3174
ISSN-Digital
1573-1375
Type
journal article
Date Issued
2010-09-20
Author(s)
Colangelo, Dominik
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.
Language
English
Keywords
Implied Volatility
Implied Volatility Surface
Option Pricing
Forecasting
Tree Boosting
Regression Tree
Functional Gradient Descent
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer Science
Publisher place
Dordrecht
Volume
20
Number
4
Start page
421
End page
434
Pages
14
Subject(s)
Division(s)
Eprints ID
53839