Diffusion Copulas: Identification and Estimation

Ruijun Bu, Kaddour Hadri () and Dennis Kristensen

Papers from arXiv.org

Abstract: We propose a new semiparametric approach for modelling nonlinear univariate diffusions, where the observed process is a nonparametric transformation of an underlying parametric diffusion (UPD). This modelling strategy yields a general class of semiparametric Markov diffusion models with parametric dynamic copulas and nonparametric marginal distributions. We provide primitive conditions for the identification of the UPD parameters together with the unknown transformations from discrete samples. Likelihood-based estimators of both parametric and nonparametric components are developed and we analyze the asymptotic properties of these. Kernel-based drift and diffusion estimators are also proposed and shown to be normally distributed in large samples. A simulation study investigates the finite sample performance of our estimators in the context of modelling US short-term interest rates. We also present a simple application of the proposed method for modelling the CBOE volatility index data.

Date: 2020-05
New Economics Papers: this item is included in nep-dcm and nep-ore
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http://arxiv.org/pdf/2005.03513 Latest version (application/pdf)

Related works:
Journal Article: Diffusion copulas: Identification and estimation (2021)
Working Paper: Diffusion Copulas: Identification and Estimation (2018)
Working Paper: Diffusion Copulas: Identification and Estimation (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2005.03513

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