正态逆高斯扩散模型的MCMC估计

使用贝叶斯方法估计了正态逆高斯扩散模型,该方法首先使用Eu ler方法对连续过程进行离散化,用离散过程的似然函数做为模型参数的近似似然函数。证明了M CM C方法是分析正态逆高斯扩散模型的有效工具,由M CM C方法抽样所得的后验分布可以用来进行统计推断。模拟试验表明:正态逆高斯扩散能够体现资产收益的许多经验特征,如泰勒效应、尖峰厚尾等。

Estimation of Normal Inverse Gaussian Diffusion Using MCMC Method

In this paper we propose a Bayesian method to estimate the normal inverse Gaussian(NIG) diffusion model.The approach is based on the Markov chain Monte Carlo(MCMC) method with the likelihood of the discredited process as the approximate posterior likelihood.We demonstrate that the MCMC method provides a useful tool in analyzing NIG diffusion.In particular,quantities of posterior distributions obtained from the MCMC outputs can be used for statistical inference.The MCMC method is based on Euler scheme.Our simulation study shows that the NIG diffusion exhibits many of the stylized facts about asset returns documented in the discrete-time financial econometrics literature,such as the Taylor effect,a slowly declining autocorrelation function of the squared returns,and thick tails.