Nonparametric inference of discretely sampled stable Lévy processes

Zhibiao Zhao () and Wei Biao Wu

Journal of Econometrics, 2009, vol. 153, issue 1, 83-92

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 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.

Keywords: Bahadur-Kiefer; representation; Levy; process; Nonparametric; estimation; Quantile; regression; Spot; volatility; Stable; index; Stable; process (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:153:y:2009:i:1:p:83-92

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