Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5097169 | Journal of Econometrics | 2009 | 10 Pages |
Abstract
We study nonparametric inference of stochastic models driven by stable Lévy processes. We introduce a nonparametric estimator of the stable index that achieves the parametric n rate of convergence. For the volatility function, due to the heavy-tailedness, the classical least-squares method is not applicable. We then propose a nonparametric least-absolute-deviation or median-quantile estimator and study its asymptotic behavior, including asymptotic normality and maximal deviations, by establishing a representation of Bahadur-Kiefer type. The result is applied to several major foreign exchange rates.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Zhibiao Zhao, Wei Biao Wu,