@techreport{Fengler2003Fitting,
abstract = {Nonparametric methods for estimating the implied volatility surface or the implied
volatility smile 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, in the second
step the actual fitting algorithm is applied. These two-step estimators may be seriously
biased when option prices are observed with measurement errors. Moreover,
after the nonlinear transformation of the option prices the error distribution will be
complicated and less tractable. In this study, we propose a one-step estimator for the
implied volatility surface based on a least squares kernel smoother of the Black-Scholes
formula. Consistency and the asymptotic distribution of the estimate are provided.
We demonstrate the estimator using German DAX index option data to recover the
smile and the implied volatility surface.},
author = {Matthias R. Fengler and Qihua Wang},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330; implied volatility surface; smile; Black-Scholes formula; least squares kernel smoothing; Black-Scholes-Modell; Optionspreistheorie; Volatilit\"{a}t; Methode der kleinsten Quadrate; Sch\"{a}tzung; Index-Futures; Sch\"{a}tzung; Theorie; Deutschland},
language = {eng},
note = {urn:nbn:de:kobv:11-10050259},
number = {2003,25},
title = {Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface},
type = {Discussion papers of interdisciplinary research project 373},
url = {http://hdl.handle.net/10419/22240},
year = {2003}
}