基于Skewed-t分布的FIGARCH模型与VaR的度量

金融资产收益率序列的波动具有典型的尖峰厚尾和非对称性特征,描述这种特性需以合适的概率分布函数为基础.因此,寻求更好的概率分布函数对风险度量、VaR的计算有着十分重要的意义.有鉴于此引入Skewed-t分布度量VaR,并比较分析了RiskMetrics及FIGARCH类模型度量VaR值的准确程度,本文同时分析了多头头寸和空头头寸情况下的VaR.结果表明,在两种头寸情况下,Skewed-t分布在空头和多头情形对资产厚尾特性以及非对称性的拟合效果均要比正态分布好;在两种头寸中不同的置信水平下,FIAGARCH(CHUNG)模型预测的VaR值改进了使用传统模型的精确性,高估或低估风险的程度较轻.     

基于EGB2分布族的GAS-EGARCH模型与VaR预测

GARCH族模型是刻画资产收益率的常用工具,在风险度量领域具有广泛应用。为了更有效地描述收益率的偏斜厚尾等特征,越来越多学者对GARCH族模型的条件分布形式进行了研究。但是仅对GARCH模型条件分布进行修正是不够的,还需要对模型本身的函数形式进行修正。基于得分函数的时变参数建模思想近年来受到广泛关注,本文借助这一思想对EGARCH模型中对数标准差进行时变波动建模,并利用EGB2分布族作为模型的条件分布,进而建立GAS-EGARCH-EGB2模型。以我国10只中证行业指数为研究对象考察GAS-EGARCH-EGB2模型的风险预测效果,GAS-EGARCH-EGB2模型样本外VaR预测表现普遍优于ACM-EGARCH-EGB2模型。     

基于非参数分布的沪深股市VaR测度

沪深大盘指数的收益率分布函数并不服从通常人们所认为的正态分布.因此,采用一种新的方法—非参数核密度估计,对沪深股指收益率分布进行拟合.该方法不仅很好地刻画了收益率分布的尖峰和肥尾特征,而且由此建立的VaR模型比一般的基于参数分布的VaR模型更能捕捉市场的风险特征,结论也更加准确.     

证券市场指数的尖峰厚尾特征与风险量估计

在对DOW,Nasdaq,S&P500和FTSE100等四个证券市场指数进行实证分析基础上,展示了证券市场指数的对数收益率具有尖峰厚尾的分布特征,并利用Logistic分布得到了很好的拟合,同时给出了基于Logistic分布的风险量VaR和CVaR的估计公式,以此计算证券市场指数的对数收益率的风险量VaR和CVaR的估计值.     

基于GARCH族模型的沪深300股指VaR度量

本文在修正了沪深300股票指数收益率序列的非平稳性和自身相关性之后,把ARMA模型与GARCH模型、GJR模型、IGARCH模型、FIGARCH模型、FIEGARCH模型、FIAPARCH模型、HYGARCH模型相结合,然后依次假设残差分布服从正态分布、t分布和偏t分布,来描述沪深300股票指数日对数收益率序列的尖峰厚尾性、杠杆效应和长记忆特性,利用上述模型分别计算沪深300股票指数的VaR值.在空头和多头投资者情况下,不同的波动性模型和不同残差分布的VaR预测有效性差距很大.比较得知,在不同的置信水平下,沪深300股票指数收益率序列空头和多头的VaR预测成功概率比较高的模型有HYGARCH和FIEGARCH这两类具有长记忆性的模型.     

双边Burr分布及其在金融市场风险预测与优化中的应用

考虑到金融数据具有非对称、尖峰厚尾特征,文章将具有尖峰厚尾特征的Burr分布拓展至双边Burr(TSB)分布,给出了其重要的数字特征、极大似然估计、最小二乘估计以及加权最小二乘估计,并通过数值模拟验证了这三种参数估计方法的有效性.其次,文章基于TSB分布构建GJR-GARCH模型,旨在研究TSB分布相比于常见分布在度量金融风险方面的优势.实证结果表明,与正态分布、t分布、GED分布、双边Weibull分布和双边Lomax分布相比,基于该分布的GJR-GARCH模型具有最高的VaR预测精度.另外,文章将基于TSB分布的GJR-GARCH模型与Copula函数结合来构建均值-CVaR模型以研究多元投资组合的风险优化,实证研究亦表明能够刻画非对称特征的该模型具有更好的CVaR预测效果.最后,稳健性检验结果证实TSB分布对于金融风险预测以及投资组合优化的改进效果不依赖于波动率模型和Copula函数的设定.     

商业银行操作风险的度量——基于非参数方法

在损失分布方法的基础上,本文基于非参数方法对商业银行操作风险的度量进行了研究。非参数方法对损失额的分布不作过多的设定,避免了由于分布误设可能出现的偏差。古典的核密度估计对损失额拟合的效果不太好,特别是尾部的拟合效果更差。变换后的核密度估计的拟合效果比古典的核密度估计改善很多.基于变换后的核密度估计对商业银行操作风险损失度量可以得到不同置信水平的VaR与ES,并且不同置信水平的差距比较大。基于非参数与基于参数方法得到的各个置信水平的VaR与ES有一定差距。     

VaR最小化的股指期货套期保值比率研究

以VaR最小化为目标,结合波动率预测建立套期保值模型,充分反应了金融收益率尖峰厚尾和波动聚集的特征。通过对沪深300股指期货的日结算数据实证研究发现,在现货组合与股指期货高相关性的条件下,VaR最小化套期保值较最小方差套期保值能进一步降低组合样本外收益率的VaR值,EWMA与Cornish-Fisher展开相结合的方法能取得最好的VaR最小化套期保值效果。     

基于M-H抽样的贝叶斯非对称厚尾GARCH模型研究

针对非对称厚尾GARCH模型参数的预选分布很难确定的问题。对模型参数空间进行数据扩张,把模型中的厚尾残差分布表示成正态分布和逆伽玛分布的混合分布,然后通过对参数的后验条件分布进行变换获得参数的预选分布,从而利用M-H抽样实现了非对称厚尾GARCH模型的贝叶斯分析。中国原油收益率波动的实证研究发现中国原油收益率的波动具有高峰厚尾性但不存在"杠杆效应",样本内的预测评价发现基于M-H抽样的贝叶斯方法优于极大似然方法,说明了M-H抽样方案设计的有效性。     

基于周期GARCH过程VaR的分位回归估计

近几年来,风险价值(VaR)已成为金融市场风险度量及风险管理的标准工具.文章用周期广义自回归条件异方差(GARCH)模型拟合金融市场数据,并应用分位回归方法得到此模型参数及条件VaR的估计,在一定条件下估计具有强相合性及渐近正态性,蒙特卡罗模拟结果表明此方法具有稳健性,且对于条件VaR的预测具有很高的准确性,沪深300指数的实证分析结果表明此方法关于VaR的预测具有非常好的效果.     

Bayesian analysis of loss reserving using dynamic models with generalized beta distribution

A Bayesian approach is presented in order to model long tail loss reserving data using the generalized beta distribution of the second kind (GB2) with dynamic mean functions and mixture model representation. The proposed GB2 distribution provides a flexible probability density function, which nests various distributions with light and heavy tails, to facilitate accurate loss reserving in insurance applications. Extending the mean functions to include the state space and threshold models provides a dynamic approach to allow for irregular claims behaviors and legislative change which may occur during the claims settlement period. The mixture of GB2 distributions is proposed as a mean of modeling the unobserved heterogeneity which arises from the incidence of very large claims in the loss reserving data. It is shown through both simulation study and forecasting that model parameters are estimated with high accuracy.     

基于左截尾g-h分布的船舶溢油损失分布拟合研究

由于船舶溢油损失对保险公司的影响非常大,保险费率厘定过程中对损失分布拟合的研究非常重视.以全国船舶溢油事故损失数据为样本,对船舶溢油损失序列的分布特征进行研究.通过实证研究发现,船舶溢油损失数据不服从正态分布,具有"右偏、尖峰、厚尾"的特性.考虑船舶溢油损失数据的左截尾特性,将左截尾g-h分布引入船舶溢油损失序列的分布拟合中,结果表明,这种分布对船舶溢油损失数据的拟合效果与传统分布相比更好.     

基于缓冲超越概率的风险度量及套期保值

尾部风险测度是风险管理中的关键点,本文利用缓冲超越概率模型,量化不同预期损失的风险概率分布,构建条件风险价值约束下的最小化“厚尾事件”概率的套期保值策略,从而将现有研究的视角拓展到考虑预期损失和风险概率的双重维度。本文通过实证数据统计和参数化拟合分布两个方法提供不同风险阈值及对应的缓冲超越概率的稳定解集合,研究结果发现,无论预期损失服从厚尾分布还是正态分布,缓冲超越概率模型均能够显著地降低市场风险和潜在的“厚尾事件”发生的概率,并提供比最小化方差稳定的套期保值比率。     

基于Expectile和Realized GARCH模型的波动率预测

Realized GARCH模型是预测波动率的经典模型之一,最小化非对称二次损失函数的Expectile对收益率尾部分布更加敏感,我们在Realized GARCH模型的基础上引入Expectile提出Expectile-Realized GARCH模型。以沪深300指数的高频收益率为例建模分析,对比不同模型下的波动率预测效果,发现Expectile-Realized GARCH模型较Realized GARCH模型对波动率预测能力更好。其中,当风险水平为95%时,对应的Expectile-Realized GARCH波动率预测能力最好。     

The impact of a heavy-tailed service-time distribution upon the M/GI/s waiting-time distribution

By exploiting an infinite-server-model lower bound, we show that the tails of the steady-state and transient waiting-time distributions in the M/GI/s queue with unlimited waiting room and the first-come first-served discipline are bounded below by tails of Poisson distributions. As a consequence, the tail of the steady-state waiting-time distribution is bounded below by a constant times the sth power of the tail of the service-time stationary-excess distribution. We apply that bound to show that the steady-state waiting-time distribution has a heavy tail (with appropriate definition) whenever the service-time distribution does. We also establish additional results that enable us to nearly capture the full asymptotics in both light and heavy traffic. The difference between the asymptotic behavior in these two regions shows that the actual asymptotic form must be quite complicated. This revised version was published online in June 2006 with corrections to the Cover Date.     

The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term

Within a competitive electric power market, electricity price is one of the core elements, which is crucial to all the market participants. Accurately forecasting of electricity price becomes highly desirable. This paper propose a forecasting model of electricity price using chaotic sequences for forecasting of short term electricity price in the Australian power market. One modified model is applies seasonal adjustment and another modified model is employed seasonal adjustment and adaptive particle swarm optimization (APSO) that determines the parameters for the chaotic system. The experimental results show that the proposed methods performs noticeably better than the traditional chaotic algorithm.     

Weak properties and robustness of t-Hill estimators

We describe a novel method of heavy tails estimation based on transformed score (t-score). Based on a new score moment method we derive the t-Hill estimator, which estimates the extreme value index of a distribution function with regularly varying tail. t-Hill estimator is distribution sensitive, thus it differs in e.g. Pareto and log-gamma case. Here, we study both forms of the estimator, i.e. t-Hill and t-lgHill. For both estimators we prove weak consistency in moving average settings as well as the asymptotic normality of t-lgHill estimator in iid setting. In cases of contamination with heavier tails than the tail of original sample, t-Hill outperforms several robust tail estimators, especially in small samples. A simulation study emphasizes the fact that the level of contamination is playing a crucial role. The larger the contamination, the better are the t-score moment estimates. The reason for this is the bounded t-score of heavy-tailed distributions (and, consequently, bounded influence functions of the estimators). We illustrate the developed methodology on a small sample data set of stake measurements from Guanaco glacier in Chile.     

城市表层土壤重金属污染分析模型

本文先利用Matlab做出各种重金属元素浓度的空间分布图,初步得到土壤重金属污染的状况.接着用内梅罗污染指数法定量的确定土壤重金属污染最严重的地区,并用主成分分析法进行了验证.最后利用灰色-灾变与回归预测的组合模型解决了地质环境的演变问题.     

Asymptotical analysis of negative insurance premiums in critical situations

The paper considers the risks of the insurance companies that conduct purely rent operations. The asymptotic of certain distribution functions are found in the case of a heavy traffic and regular tail variation of the premium distribution function.     

Likelihood Based Confidence Intervals for the Tail Index

For the estimation of the tail index of a heavy tailed distribution, one of the well-known estimators is the Hill estimator (Hill, 1975). One obvious way to construct a confidence interval for the tail index is via the normal approximation of the Hill estimator. In this paper we apply both the empirical likelihood method and the parametric likelihood method to obtaining confidence intervals for the tail index. Our limited simulation study indicates that the normal approximation method is worse than the other two methods in terms of coverage probability, and the empirical likelihood method and the parametric likelihood method are comparable.