Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm
Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm
复制标题
通过混合蒙特卡罗算法实现随机波动率模型的贝叶斯估计
DOI：
10.1088/1742-6596/490/1/012092
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Tetsuya Takaishi
中科院分区：
文献类型：
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作者：
Tetsuya Takaishi and Toshiaki Watanabe;Ｔｅｔｓｕｙａ Ｔａｋａｉｓｈｉ;Ｔｅｔｓｕｙａ Ｔａｋａｉｓｈｉ;Tetsuya Takaishi
采用混合蒙特卡罗算法（HMCA）对实现的随机波动率（RSV）模型进行贝叶斯参数估计。利用二阶最小范数积分器（2MNI）进行HMCA分子动力学（MD）模拟，我们发现2MNI比传统的跨越式积分器更有效。我们还发现，用HMCA采样的波动变量的自相关时间很短。因此，具有2MNI的HMCA是一种有效的RSV模型参数估计算法。
The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model.