Empirical Analysis ofStochastic Volatility Model by Hybrid Monte Carlo Algorithm
Empirical Analysis ofStochastic Volatility Model by Hybrid Monte Carlo Algorithm
复制标题
混合蒙特卡罗算法随机波动模型的实证分析
DOI：
10.1088/1742-6596/423/1/012021
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发表时间：
2013
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：
Kuroda;M.;Mori;Y.;Iizuka;M.;Sakakihara;M.;松居俊宏（飯塚誠也）;T.Takaishi
随机波动率模型是一种能够推断资产收益潜在波动率的波动率模型。随机波动率（SV）模型的贝叶斯推断是由混合蒙特卡罗（HMC）算法，这是上级其他马尔可夫链蒙特卡罗方法在采样波动率变量。我们执行的SV模型的HMC模拟在东京证券交易所交易的两个流动性股票收益率和测量这些股票收益率的波动性。然后，我们计算的波动率测量的准确性，使用已实现的波动率作为代理的真实波动率，并比较SV模型和Gestival模型，这是其他波动率模型之一。使用已实现波动率计算的准确性，我们发现，经验SV模型的表现优于Gestival模型。
The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.