共查询到18条相似文献,搜索用时 62 毫秒
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与VaR金融风险测度相比,CVaR具有更好的数理性质,其计算方法成为关注的焦点。相对于单期CVaR而言,多期CVaR风险测度具有较强的非线性特征,其建模过程更加复杂。在神经网络分位数回归基础上,建立了一种新的多期CVaR风险测度方法;基于似然比检验,建立了多期CVaR风险测度返回测试评价准则。将该新方法应用于沪深300指数的多期CVaR风险测度,并将其与传统的测度方法进行了对比,返回测试结果表明:第一,该新方法具有较强的稳健性,各期平均绝对误差大小基本不变,特别适合于多期CVaR风险测度;第二,基于神经网络分位数回归的多期CVaR风险测度效果优于传统测度方法,表现为似然比检验拒绝次数最少和平均绝对误差最小。 相似文献
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伴随中国原油期货的上市,作为商品期货最大交易单品的原油期货,其套期保值功能必将成为新的研究热点。本文采用skew-t-GARCH(1,1)模型捕捉原油期现货收益率的"波动集聚"和"尖峰厚尾"特性,在此基础上通过构造Copula分位数套保模型研究不同原油市场状态下(牛市、熊市)的套保比率及效率。利用蒙特卡洛模拟对线性、Normal Copula及T-Copula分位数回归模型进行效率比较,并对英国Brent和美国WTI原油期货收益率进行实证研究,结果表明:①不同市场状态下,原油期货的最优套保比率具有非对称性;②T-Copula分位数回归模型的尾部套期保值效率更稳定。因此,利用原油期货进行规避风险时,要根据市场行情合理运用套期保值模型。 相似文献
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众所周知,房地产业与银行业是高度相关的,如何确定银行业股票收益率对房地产业股票收益率的影响以及如何根据银行业股票收益率预测房地产业股票收益率的波动是非常重要的问题。本文首先使用Copula分位数回归建立了银行业股票收益率对房地产业股票收益率的回归模型,并且给出了Copula分位数回归基础上的CopuIa选择新标准,即分位数损失函数距离意义下的Copula函数选择准则,依据该准则我们选取Clayton Copula分位数回归模型刻画了低迷时期银行业股票收益率如何影响房地产业股票收益率,并据此对房地产业股票收益率的波动进行了预测 相似文献
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考虑线性系统模型 y=x'β_0 x'r_0e, (1)其中x为P维已知向量,β_0是未知的P维回归系数,e是1维不可观察的误差随机变量。 y在x下的条件u分位数可以写成: 相似文献
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利用分位数回归模型探讨了原油价格、道琼斯指数、美元汇率、上证指数和利率对国内黄金价格的影响.实证分析结果表明在不同分位数水平上各因素对黄金价格的影响不一样.分位数回归能够从历史数据中挖掘出更多的信息,更有利于投资者了解影响黄金价格波动的因素从而做出更好的决策. 相似文献
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现有GARCH模型依赖于参数条件分布形式假设,依然不能有效刻画金融资产收益偏态厚尾特性,分位数回归能给条件分布提供更加全面的描述.在分位数回归和GJR-GARCH模型基础上建立分位数GJR-GARCH模型,并在贝叶斯框架下对模型进行分析;同时利用中国金融市场数据检验分位数GJR-GARCH模型在风险价值预测方面的实际效果. 相似文献
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T. Rodrigues J.-L. Dortet-Bernadet 《Journal of computational and graphical statistics》2013,22(3):732-746
We describe a Bayesian model for simultaneous linear quantile regression at several specified quantile levels. More specifically, we propose to model the conditional distributions by using random probability measures, known as quantile pyramids, introduced by Hjort and Walker. Unlike many existing approaches, this framework allows us to specify meaningful priors on the conditional distributions, while retaining the flexibility afforded by the nonparametric error distribution formulation. Simulation studies demonstrate the flexibility of the proposed approach in estimating diverse scenarios, generally outperforming other competitive methods. We also provide conditions for posterior consistency. The method is particularly promising for modeling the extremal quantiles. Applications to extreme value analysis and in higher dimensions are also explored through data examples. Supplemental material for this article is available online. 相似文献
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A two-stage approach is proposed to overcome the problem in quantile regression, where separately fitted curves for several quantiles may cross. The standard Bayesian quantile regression model is applied in the first stage, followed by a Gaussian process regression adjustment, which monotonizes the quantile function while borrowing strength from nearby quantiles. The two-stage approach is computationally efficient, and more general than existing techniques. The method is shown to be competitive with alternative approaches via its performance in simulated examples. Supplementary materials for the article are available online. 相似文献
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在介绍分位数回归方法的基础上,讨论了分位数回归模型在研究影响大学生成绩变化因素方面的应用,与最小二乘回归模型对比,分位数回归能有效地描述数据尾端的分布情况. 相似文献
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《Journal of computational and graphical statistics》2013,22(3):497-513
The SiZer methodology proposed by Chaudhuri and Marron (1999) is a valuable tool for conducting exploratory data analysis. Since its inception different versions of SiZer have been proposed in the literature. Most of these SiZer variants are targeting the mean structure of the data, and are incapable of providing any information about the quantile composition of the data. To fill this need, this article proposes a quantile version of SiZer for the regression setting. By inspecting the SiZer maps produced by this new SiZer, real quantile structures hidden in a dataset can be more effectively revealed, while at the same time spurious features can be filtered out. The utility of this quantile SiZer is illustrated via applications to both real data and simulated examples. This article has supplementary material online. 相似文献
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The frequentist model averaging(FMA) and the focus information criterion(FIC) under a local framework have been extensively studied in the likelihood and regression setting since the seminal work of Hjort and Claeskens in 2003. One inconvenience, however, of the existing works is that they usually require the involved criterion function to be twice differentiable which thus prevents a direct application to the case of quantile regression(QR). This as well as some other intrinsic merits of QR mot... 相似文献
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Yu-nan Su Mao-zai Tian Center for Applied Statistics School of Statistics Remin University of China Beijing China 《应用数学学报(英文版)》2011,(3)
In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our ... 相似文献
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