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1.
基于GARCH模型的石油价格变动模拟 总被引:1,自引:1,他引:0
石油是一种特殊的商品,是国家重要的战略物资,世界各国都十分重视其价格变动问题,因为油价变化会影响到各国经济发展,甚至国家安全。因此,本文采用GARCH模型,通过基于Gibbs抽样的MCMC方法分析了国际市场石油价格的分布特征,对石油价格波动的异方差特性进行描述和模拟,实证分析结果说明从石油价格波动序列峰度系数和平方价格波动序列自相关函数的描述来看,基于t分布的模型模拟效果优于基于正态分布的模型,这一结论反映了石油价格波动序列的分布特性。 相似文献
2.
Huseyin Ince 《Computational Management Science》2006,3(2):161-174
The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore,
it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare
with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven
non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock
Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain
this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks
overperform the SVR and time series model (GARCH). 相似文献
3.
The major goal of this paper is to examine the hypothesis that stock returns and return volatility are asymmetric, threshold nonlinear, functions of change in trading volume. A minor goal is to examine whether return spillover effects also display such asymmetry. Employing a double-threshold GARCH model with trading volume as a threshold variable, we find strong evidence supporting this hypothesis in five international market return series. Asymmetric causality tests lend further support to our trading volume threshold model and conclusions. Specifically, an increase in volume is positively associated, while decreasing volume is negatively associated, with the major price index in four of the five markets. The volatility of each series also displays an asymmetric reaction, four of the markets display higher volatility following increases in trading volume. Using posterior odds ratio, the proposed threshold model is strongly favored in three of the five markets, compared to a US news double threshold GARCH model and a symmetric GARCH model. We also find significant nonlinear asymmetric return spillover effects from the US market. 相似文献
4.
中国股票市场波动特性的实证研究 总被引:4,自引:0,他引:4
倪杰 《数学的实践与认识》2003,33(9):50-54
本文以上证综指和深成分指数的日收益率为研究对象 ,应用 GARCH、TARCH模型理论 ,进一步分析了日收益率波动的条件异方差性、非对称性 ,同时比较了两个股票市场的不同波动特征 相似文献
5.
代理理论认为,在动荡和不确定环境下,管理者会做出偏离企业价值最大化的非效率投资行为。为探究金融冲击这个带有不确定色彩的因素,是否会恶化企业非效率投资行为,论文先是使用一个数学模型来说明金融冲击与非效率投资的可能关系,而后以GARCH方法的条件异方差来度量金融冲击的潜变量股市冲击、货币冲击和汇率冲击。最后,实证检验了这三个金融冲击潜变量和非效率投资的关系。研究发现:货币冲击和汇率冲击会显著提升非效率投资,而股市冲击能够显著降低非效率投资。进一步研究表明,货币冲击和汇率冲击会通过显著增加过度投资来加大非效率投资行为,而股市冲击则通过显著减少投资不足来达到降低非效率投资行为。 相似文献
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7.
This paper presents a new value at risk (VaR) estimation model for equity returns time series and tests it extensively on Stock Indices of 14 countries. Two most important stylized facts of such series are volatility clustering, and non-normality as a result of fat tails of the return distribution. While volatility clustering has been extensively studied using the GARCH model and its various extensions, the phenomenon of non-normality has not been comprehensively explored, at least in the context of VaR estimation. A combination of extreme value theory (EVT) and GARCH has been explored to analyze financial data showing non-normal behavior. This paper proposes a combination of the Pearson’s Type IV distribution and the GARCH (1, 1) approach to furnish a new method with superior predictive abilities. The approach is back tested for the entire sample as well as for a holdout sample using rolling windows. 相似文献
8.
This paper examines the extent to which financial returns on market indices exhibit mean and volatility asymmetries, as a response to past information from both the U.S. market and the local market itself. In particular, we wish to assess the asymmetric effect of a combination of local and U.S. market news on volatility. To the best of the authors knowledge, this joint effect has not been considered previously. We propose a double threshold non‐linear heteroscedastic model, combined with a GJR‐GARCH effect in the conditional volatility equation, to capture jointly both mean and volatility asymmetric behaviours and the interactive effect of U.S. and local market news. In an application to five major international market indices, clear evidence of threshold non‐linearity is discovered, supporting the hypothesis of an uneven mean‐reverting pattern and volatility asymmetry, both in reaction to U.S. market news and news from the local market itself. Significant, but somewhat different, interactive effects between local and U.S. news are observed in all markets. An asymmetric pattern in the exogenous relationship between the local market and the U.S. market is also found. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
9.
本文分析中国上海证券市场回报率。分别通过APdMA模型和GARCH模型,发现若用APdMA模型分析和建立时间序列模型,一次自回归项是不够的,需要高次项,在大多数情形,若运用GARCH模型,则GARCH(1,1)就能够很好的拟合数据。 相似文献
10.
We study a CUSUM–type monitoring scheme designed to sequentially detect changes in the regression parameter of an underlying
linear model. The test statistic used is based on recursive residuals. Main aim of this paper is to derive the limiting extreme
value distribution under the null hypothesis of structural stability. The model assumptions are flexible enough to include
rather general classes of error sequences such as augmented GARCH(1,1) processes. The result is underlined by an illustrative
simulation study.
Research partially supported by NSF grants DMS–0604670 and DMS–065242. 相似文献