Diffusion copulas: Identification and estimation
Diffusion copulas: Identification and estimation
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DOI：
10.1016/j.jeconom.2020.06.004
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发表时间：
2020-05
影响因子：
6.3
通讯作者：
Ruijun Bu;K. Hadri;Dennis Kristensen
中科院分区：
文献类型：
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作者：
Ruijun Bu;K. Hadri;Dennis Kristensen
我们提出了一种新的半参数方法来模拟非线性单变量扩散，其中观测过程是基础参数扩散(UPD)的非参数变换。这一建模策略得到了一类具有参数动态Copula和非参数边际分布的半参数马尔可夫扩散模型。我们给出了识别UPD参数的原始条件以及离散样本的未知变换。给出了参数分量和非参数分量的似然估计，并分析了它们的渐近性质。基于核的漂移和扩散估计也被提出，并在大样本中被证明为正态分布。一项模拟研究在模拟美国短期利率的背景下调查了我们估计者的有限样本表现。我们还给出了该方法在芝加哥期权交易所波动率指数数据建模中的简单应用。
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.