Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s11222-009-9134-y | Statistics and Computing |
Keywords | Field | DocType |
Implied volatility,Implied volatility surface,Option pricing,Forecasting,Tree boosting,Regression tree,Functional gradient descent | Econometrics,Decision tree,Implied volatility,Valuation of options,Optimal stopping,Regression,Boosting (machine learning),Semiparametric model,Forward volatility,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 4 | 0960-3174 |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Audrino | 1 | 14 | 3.36 |
| Dominik Colangelo | 2 | 1 | 0.35 |