2020/05/07 by Ruijun Bu, Bu, Ruijun, Kaddour Hadri +3
Economics, Econometrics and Finance · Mathematics · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Statistical Methods and Inference #Stochastic processes and financial applications #econ.EM #stat.ME
paper · pdf · doi:10.48550/arxiv.2005.03513
arxiv created 2020/05/07 · openalex publication_date 2020/05/07 · arxiv updated 2020/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.