英文标题：
《Analysis of Spin Financial Market by GARCH Model》
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作者：
Tetsuya Takaishi
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最新提交年份：
2014
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英文摘要：
A spin model is used for simulations of financial markets. To determine return volatility in the spin financial market we use the GARCH model often used for volatility estimation in empirical finance. We apply the Bayesian inference performed by the Markov Chain Monte Carlo method to the parameter estimation of the GARCH model. It is found that volatility determined by the GARCH model exhibits \"volatility clustering\" also observed in the real financial markets. Using volatility determined by the GARCH model we examine the mixture-of-distribution hypothesis (MDH) suggested for the asset return dynamics. We find that the returns standardized by volatility are approximately standard normal random variables. Moreover we find that the absolute standardized returns show no significant autocorrelation. These findings are consistent with the view of the MDH for the return dynamics.
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中文摘要：
自旋模型用于模拟金融市常为了确定自旋金融市场中的收益波动率，我们使用了实证金融中经常用于波动率估计的GARCH模型。我们将马尔可夫链蒙特卡罗方法进行的贝叶斯推断应用于GARCH模型的参数估计。研究发现，由GARCH模型确定的波动率在真实金融市场中也表现出“波动聚类”。利用GARCH模型确定的波动率，我们检验了资产收益动态的混合分布假设（MDH）。我们发现，由波动率标准化的收益率近似为标准正态随机变量。此外，我们发现绝对标准化收益率没有显著的自相关。这些发现与MDH对回归动态的看法一致。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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