Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface
Series
Discussion Paper
Type
working paper
Date Issued
2003
Author(s)
Abstract
Nonparametric methods for estimating the implied volatility surface are very popular, since they do not impose a specific functional form on the estimate. Traditionally, these methods are two-step estimators. The first step requires to extract implied volatility data from observed option prices, then the actual fitting algorithm is applied. These two-step estimators may be seriously biased when option prices are observed with measurement errors. Moreover, the nonlinear transformation of the option prices will make the error distribution less tractable. In this study, we propose a new one-step estimator for the implied volatility surface based on a least squares kernel smoother of the Black-Scholes formula. We demonstrate the estimator using German DAX index option data to recover the implied volatility surface.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=433200
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=433200
Language
English
Keywords
implied volatility surface
smile
Black-Scholes formula
least squares kernel smoothing
HSG Classification
not classified
Refereed
No
Publisher
SEPS, University of St. Gallen, Switzerland (St. Gallen)
Subject(s)
Eprints ID
216406