Estimation of stochastic volatility models via Monte Carlo maximum likelihood
Estimation of stochastic volatility models via Monte Carlo maximum likelihood
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DOI：
10.1016/s0304-4076(98)00016-5
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发表时间：
1998-12
影响因子：
6.3
通讯作者：
G. Sandmann;S. J. Koopman
中科院分区：
文献类型：
--
作者：
G. Sandmann;S. J. Koopman
本文讨论了随机波动率模型的MonteCarlo极大似然估计方法。基本SV模型可以表示为具有对数卡方扰动的线性状态空间模型。通过将似然函数分解为由卡尔曼滤波器构造的高斯部分和由仿真评估其期望的剩余函数，可以任意精确地近似似然函数。当基本SV模型在应用实证研究中可能出现的一些方向上扩展时，不需要修改此估计程序。这与其他方法相比是有利的。新估计的有限样本性能是相当的蒙特卡罗马尔可夫链（MCMC）方法。
This paper discusses the Monte Carlo maximum likelihood method of estimating stochastic volatility (SV) models. The basic SV model can be expressed as a linear state space model with log chi-square disturbances. The likelihood function can be approximated arbitrarily accurately by decomposing it into a Gaussian part, constructed by the Kalman filter, and a remainder function, whose expectation is evaluated by simulation. No modifications of this estimation procedure are required when the basic SV model is extended in a number of directions likely to arise in applied empirical research. This compares favorably with alternative approaches. The finite sample performance of the new estimator is shown to be comparable to the Monte Carlo Markov chain (MCMC) method.