Article ID Journal Published Year Pages File Type
417207 Computational Statistics & Data Analysis 2008 9 Pages PDF
Abstract

The Nadaraya–Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data. This sensitivity can be reduced, for example, by using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. The asymptotic distribution of the proposed estimator is derived under mild ββ-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical finite-sample improvements. Finally, the proposed method is applied to the modelling of implied volatility.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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