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Diffusion copulas: Identification and estimation

Bu, R; Hadri, K; Kristensen, D; (2020) Diffusion copulas: Identification and estimation. Journal of Econometrics 10.1016/j.jeconom.2020.06.004. (In press).

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Abstract

© 2020 Elsevier B.V. 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 analyse their asymptotic properties. 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.

Type: Article
Title: Diffusion copulas: Identification and estimation
DOI: 10.1016/j.jeconom.2020.06.004
Publisher version: https://doi.org/10.1016/j.jeconom.2020.06.004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Continuous-time model; diffusion process; copula; transformation model; identification; nonparametric; semiparametric; maximum likelihood; sieve; kernel smoothing
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/10101029
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