Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-based methods
Estimation of realized stochastic volatility models using Hamiltonian Monte Carlo-based methods
复制标题
使用基于哈密顿蒙特卡罗的方法估计已实现的随机波动率模型
DOI：
10.1007/s00180-014-0546-6
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发表时间：
2015
影响因子：
1.3
通讯作者：
Didit Budi NUGROHO and Takayuki MORIMOTO
中科院分区：
文献类型：
--
作者：
森本 孝之;川崎 能典;Takayuki MORIMOTO and Yoshinori KAWASAKI;森本 孝之;Takayuki MORIMOTO and Shuichi NAGATA;Didit Budi NUGROHO and Takayuki MORIMOTO;Takayuki MORIMOTO;Didit Budi NUGROHO and Takayuki MORIMOTO
本研究发展了Hamiltonian Monte Carlo（HMC）和Riemann流形Hamiltonian Monte Carlo（RMHMC）采样器，并与多步Metropolis-Hastings采样器进行了随机波动率（SV）估计性能的比较，实现了具有非对称效应的SV模型。在无效因子方面，实证结果表明，RMHMC采样器的参数估计性能最好，其次是多移动Metropolis-Hastings采样器。特别是，它还表明，RMHMC采样器提供了一个更大的优势，在潜在的挥发链的混合性能和计算时间比HMC采样器。
This study develops and compares performance of Hamiltonian Monte Carlo (HMC) and Riemann manifold Hamiltonian Monte Carlo (RMHMC) samplers with that of multi-move Metropolis-Hastings sampler to estimate stochastic volatility (SV) and realized SV models with asymmetry effect. In terms of inefficiency factor, empirical results show that the RMHMC sampler give the best performance for estimating parameters, followed by multi-move Metropolis-Hastings sampler. In particular, it is also shown that RMHMC sampler offers a greater advantage in the mixing property of latent volatility chains and in the computational time than HMC sampler.