Maximum Likelihood Estimation of the Cox---Ingersoll---Ross Model Using Particle Filters
Maximum Likelihood Estimation of the Cox---Ingersoll---Ross Model Using Particle Filters
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DOI：
10.1007/s10614-010-9208-0
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发表时间：
2010-06
期刊：
影响因子：
--
通讯作者：
Giuliano De Rossi
中科院分区：
文献类型：
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作者：
Giuliano De Rossi
本文展示了如何以计算有效的方式构建最大模拟似然程序，以根据多元时间序列估计 Cox-Ingersoll-Ross 模型。该估计器的优点在于，它考虑了精确的似然函数，同时避免了与 MCMC 方法相关的巨大计算负担，并且无需特殊假设某些债券收益率的测量没有误差。所提出的方法已在模拟数据上实施和测试。对于实际参数值，与流行的准最大似然方法相比，即使使用适中的模拟规模，估计器似乎也具有良好的小样本属性。模拟误差的影响似乎并没有破坏估计程序。
This paper shows how to build in a computationally efficient way a maximum simulated likelihood procedure to estimate the Cox–Ingersoll–Ross model from multivariate time series. The advantage of this estimator is that it takes into account the exact likelihood function while avoiding the huge computational burden associated with MCMC methods and without the ad hoc assumption that certain bond yields are measured without error. The proposed methodology is implemented and tested on simulated data. For realistic parameter values the estimator seems to have good small sample properties, compared to the popular quasi maximum likelihood approach, even using moderate simulation sizes. The effect of simulation errors does not seem to undermine the estimation procedure.