Bayesian Inference of Stochastic volatility Model by Hybrid Monte Carlo
Bayesian Inference of Stochastic volatility Model by Hybrid Monte Carlo
复制标题
混合蒙特卡罗随机波动率模型的贝叶斯推断
DOI：
10.1142/s0218126609005733
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
T. Takaishi
中科院分区：
文献类型：
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作者：
T. Takaishi
将混合蒙特卡罗（HMC）算法应用于随机波动率（SV）模型的贝叶斯推断。我们使用HMC算法的SV模型的波动率变量的马尔可夫链蒙特卡罗更新。首先，我们使用人工金融数据计算SV模型的参数，并将HMC算法和大都会算法的结果进行比较。我们发现，HMC算法去相关的波动性变量比大都会算法。其次，我们利用HMC算法对日经225指数的时间序列进行了实证研究。我们发现样本数据的相关性行为与人工金融数据的结果相似，并得到接近1的$\phi$值（$\phi \approximately 0.977$），这意味着时间序列具有很强的持续性的波动冲击。
The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. We find that the HMC algorithm decorrelates the volatility variables faster than the Metropolis algorithm. Second we make an empirical study for the time series of the Nikkei 225 stock index by the HMC algorithm. We find the similar correlation behavior for the sampled data to the results from the artificial financial data and obtain a $\phi$ value close to one ($\phi \approx 0.977$), which means that the time series has the strong persistency of the volatility shock.