Nonparametric inference of discretely sampled stable Lévy processes

Zhibiao Zhao, Wei Biao Wu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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 sqrt(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.

Original languageEnglish (US)
Pages (from-to)83-92
Number of pages10
JournalJournal of Econometrics
Issue number1
StatePublished - Nov 2009

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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