基于g-h分布的极值风险度量研究

依据ARMA-GJR模型构造标准残差序列,利用EVT模型拟合标准残差序列的尾部特征,进而确定样本阀值,最后结合g-h分布建立一种新的金融风险度量模型———基于ARMA-GJR-EVT-g-h的动态VaR模型。用该模型对上证综指进行实证分析,结果表明,该模型能够更合理有效地管理上证综指收益的风险,并且在高的置信水平上表现更好。     

CVaR-EVT和BMM在极端金融风险管理中的应用研究

随着风险度量一致性原则的提出,研究发现金融机构广泛采用的VaR模型存在严重不足,尤其针对分布具有厚尾特征的极端金融风险无法有效度量。本文采用极值理论（EVT）解决VaR方法的尾部度量不足问题,利用CVaR-EVT和BMM模型分析美国、香港股票市场和我国沪深两市指数18年的日收益数据,研究发现：（1）在95％置信区间及点估计中,分位数为99%的CVaR-EVT所揭示的极端风险优于VaR的估计值;且BMM方法为实施长期极端风险管理提供了有力决策依据,其回报率受分段时区的影响,期间越长,风险估计值越高;（2）模型采用ML和BS方法统计估值显示,我国股票市场极端风险尾部估计值高于香港和美国市场;但是,国内市场逐步稳定,并呈现出跟进国际市场且差距缩小的发展趋势。     

基于广义双曲线SV模型的极值风险度量研究

在金融风险的度量中,拟合分布的选取直接影响到风险度量的精度问题。针对金融收益序列的动态变化,在SV模型中引入广义双曲线学生偏t分布(SV-GHSKt)拟合金融收益序列的尖峰厚尾、不对称以及杠杆效应等特征,通过马尔科夫蒙特卡洛模拟的方法将收益率序列转化为标准残差序列,然后用极值理论的POT模型拟合标准残差序列尾部分布,进而建立一种新的金融风险度量模型———基于SV-GHSKt-POT的动态VaR模型。用该模型对上证综合指数做实证研究,结果表明,SV-GHSKt-POT的动态VaR模型能很好地模拟金融收益序列的尖峰厚尾性、波动集聚性及杠杆效应,并且能够合理有效地提高风险测度的精度,尤其在高的置信水平下表现更好。     

基于尾部指数回归方法的CVaR估计以及实证研究

 在险价值VaR是一种非常重要的金融风险度量方法,近期也有很多关于动态VaR以及条件VaR (CVaR) 等方面的研究。根据金融资产的收益率具有重尾特征这一事实,本文假定金融资产收益率服从重尾分布,并假定重尾分布的尾部指数随着收益率发生变化。本文基于尾部指数回归模型对重尾分布的尾部指数进行估计,进而得到收益率尾部数据所服从的条件分布,并首次运用该方法对条件VaR进行估计。本文对沪深300指数进行了实证研究,得到CVaR的估计,并对估计得到的CVaR的预测效果作出检验,并与传统VaR估计方法进行了对比,实证结果发现本文的方法的预测效果更好。     

基于Markov区制转换模型的极值风险度量研究

将马尔科夫区制转换模型与极值理论相结合研究金融风险度量问题.首先用SWARCH-t模型捕捉收益率序列的剧烈波动和结构变换特征,然后将收益序列转化为标准残差序列,在此基础上通过SWARCH-t模型与极值理论相结合拟合标准残差的尾部分布,进而构建基于SWARCH- t- EVT的动态VaR模型,最后对模型的有效性进行检验.研究表明,SWARCH-t-EVT模型能够有效识别上证综指的波动区制特征,且能有效合理地测度上证综指收益风险,尤其在高的置信水平下表现更好.     

A quasi-Bayesian model averaging approach for conditional quantile models

The value at risk (VaR) is a risk measure that is widely used by financial institutions to allocate risk. VaR forecast estimation involves the evaluation of conditional quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, which yields the conditional autoregressive VaR (CAViaR) models. However, uncertainty with regard to model selection in CAViaR model estimators raises the issue of identifying the better quantile predictor via averaging. In this study, we propose a quasi-Bayesian model averaging method that generates combinations of conditional VaR estimators based on single CAViaR models. This approach provides us a basis for comparing single CAViaR models against averaged ones for their ability to forecast VaR. We illustrate this method using simulated and financial daily return data series. The results demonstrate significant findings with regard to the use of averaged conditional VaR estimates when forecasting quantile risk.     

分位数回归与上证综指VaR研究

把极端分位数所具有的行为特征应用到VaR的研究中，建立上海股市收益率的条件分位数回归模型，描述其在极端分位数下的变化趋势。同时选取适当的尾部模型，并在此基础之上应用外推法预测非常极端分位数下的条件VaR，并与直接由分位数回归模型预测的结果进行比较。结果表明：两种方法得到的结果变化趋势都是一致的，由外推法预测的结果相对小一些。     

基于EVT-POT-SV模型的极值风险度量

金融市场常受各种因素的影响造成剧烈波动,资产收益也会因此产生异常变化。针对金融资产收益的厚尾性、波动的异方差性等特征,采用基于Markov链的Monte Carlo模拟积分方法,对随机波动模型进行参数估计并取得标准残差序列,应用极值理论与随机波动模型相结合,建立了基于EVT-POT-SV的动态VaR模型。通过对上证综指收益做实证分析,结果表明：该模型能很好地刻画收益序列的波动性及尾部分布特征,在度量上证综指收益的风险方面更加合理而有效。     

中国股指收益率的非对称拉普拉斯分布实证检验

应用非对称拉普拉斯分布拟合沪深两市股指日、周收益率数据。研究结果表明:非对称拉普拉斯分布能够比正态分布更好地反映两市股指的日、周收益率数据的尖峰、厚尾、偏态特征。由于非对称拉普拉斯分布有显性的表达式,便于开展参数估计和数字特征的计算,因此对于股指期货投资者而言,在计算股指收益率的VaR、CVaR进行风险测量时,采用非对称拉普拉斯分布将是较好的选择。     

制度变迁背景下人民币实际汇率趋势研究：1986-2009

 1986年至2009年间,人民币汇率制度几经变迁,人民币/美元名义汇率水平也多次呈现出跳跃性的变动,这使基于CPI等价格指数构造出的人民币/美元实际汇率指数含有明显的异动成分。本文运用异常值分析方法,分离了这段时间人民币/美元实际汇率中的异动成分,并估计了异动成分的性质和大小。这些异动都是人民币/美元名义汇率的贬值造成的实际汇率的永久性贬值。剔除这些异常变动后,人民币实际汇率序列呈现出明显的确定升值趋势。     

系统性风险度量CoES的建模和检验

本文结合极值理论(Extreme Value Theory,EVT)和新的动态混合Copula(Dynamic Mixture Copula,DM-Copula)函数,提出了一种新的CoES估计方法DM-Copula-EVT。在EVT建模中,本文改进了阈值的选取方法以避免选择的主观性,并提出了一系列新的动态混合Copula以更好地刻画金融市场日益复杂的尾部关联性。此外,本文首次提出了检验CoES模型设定正确性的后验分析方法,包括无条件覆盖性检验和条件覆盖性检验。将本文建模和检验方法应用于我国金融市场,研究发现:相对于传统使用的t分布,EVT能更好地拟合指数的尾部分布;新的动态混合Copula函数能更好地刻画金融部门与系统之间的复杂关联性。     

Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model

ABSTRACT

Conditional risk measuring plays an important role in financial regulation and depends on volatility estimation. A new class of parameter models called Generalized Autoregressive Score (GAS) model has been successfully applied for different error's densities and for different problems of time series prediction in particular for volatility modeling and VaR estimation. To improve the estimating accuracy of the GAS model, this study proposed a semi-parametric method, LS-SVR and FS-LS-SVR applied to the GAS model to estimate the conditional VaR. In particular, we fit the GAS(1,1) model to the return series using three different distributions. Then, LS-SVR and FS-LS-SVR approximate the GAS(1,1) model. An empirical research was performed to illustrate the effectiveness of the proposed method. More precisely, the experimental results from four stock indexes returns suggest that using hybrid models, GAS-LS-SVR and GAS-FS-LS-SVR provides improved performances in the VaR estimation.     

Nonparametric vector autoregression

We consider a vector conditional heteroscedastic autoregressive nonlinear (CHARN) model in which both the conditional mean and the conditional variance (volatility) matrix are unknown functions of the past. Nonparametric estimators of these functions are constructed based on local polynomial fitting. We examine the rates of convergence of these estimators and give a result on their asymptotic normality. These results are applied to estimation of volatility matrices in foreign exchange markets. Estimation of the conditional covariance surface for the Deutsche Mark/US Dollar (DEM/USD) and Deutsche Mark/British Pound (DEM/GBP) daily returns show negative correlation when the two series have opposite lagged values and positive correlation elsewhere. The relation of our findings to the capital asset pricing model is discussed.     

中国外汇储备利率风险的测度

 本文选择美元、欧元、英镑、日元四种最主要的储备货币月度利率数据作为分析对象,运用VaR方法对我国外汇储备的利率风险进行了测度。首先对序列数据进行平稳性与正态性检验,然后求出各种货币的平均收益率、相关系数矩阵与方差协方差矩阵,最后选择三种不同的币种结构求出具体的VaR值并进行比较分析。本文的结论是适当降低美元占比,并相应提高欧元、英镑、日元占比,这样可以有效地降低我国外汇储备的利率风险。     

汇率预测及其经济基本面：基于多元自适应可变窗算法的构建

在人民币国际化不断推进、人民币汇率双向波动加强的背景下，构建具有优良预测能力的人民币汇率预测模型意义重大。参数模型对汇率预测的能力不仅取决于模型设定是否正确，同时还取决于能否迅速探测参数的结构性变化以使用最佳信息估计模型参数。本文构建了多元自适应可变窗算法以及时监测模型参数的时变特征，探测最大化参数同质区间。结果显示：①在中长期（3至24个月）的美元、欧元、英镑和日元兑人民币汇率的样本外推预测中，多元自适应可变窗算法能显著优于随机游走模型、购买力平价模型、弹性货币模型、利率平价模型、泰勒规则模型与偏移型泰勒规则模型这六种汇率预测主流模型，其预测能力也显著优于实时窗宽选择算法与自回归模型；在美元兑人民币汇率中长期（3至24个月）预测中，其预测误差MAE度量相比于次优模型能降低 25%～50%。②多元自适应可变窗算法能迅速捕捉美元、欧元、英镑和日元兑人民币汇率的拐点，预测人民币汇率走向并刻画人民币汇率的周期性变化，其长期（向前9个月）方向性趋势样本外推预测精度比次优模型提高了16%～40%。③断点前后的汇率动态结构性变化显示“811”汇改促进了经济基本面对汇率预期重要性的显著提升与市场风险偏好的转变。“811”汇改之后，人民币汇率预期更易受外部冲击影响。加速利率市场化建设、提高国内收入、稳定物价、坚持带管制的浮动汇率制度与有效的资本管制相结合等措施对促进汇率市场化、防止汇率风险具有重要意义。     

Production Frontiers and Panel Data

Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, we introduce a new test of model adequacy, the dynamic quantile test. Applications to real data provide empirical support to this methodology.     

Comparing VaR Approximation Methods that Use the First Four Moments as Inputs

This article compares four methods used to approximate value at risk (VaR) from the first four moments of a probability distribution: Cornish–Fisher, Edgeworth, Gram–Charlier, and Johnson distributions. Increasing rearrangements are applied to the first three methods. Simulation results suggest that for large sample situations, Johnson distributions yield the most accurate VaR approximation. For small sample situations with small tail probabilities, Johnson distributions yield the worst approximation. A particularly relevant case would be in banking applications for calculating the size of operational risk to cover certain loss types. For this case, the rearranged Gram–Charlier method is recommended.     

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ABSTRACT

In the class of stochastic volatility (SV) models, leverage effects are typically specified through the direct correlation between the innovations in both returns and volatility, resulting in the dynamic leverage (DL) model. Recently, two asymmetric SV models based on threshold effects have been proposed in the literature. As such models consider only the sign of the previous return and neglect its magnitude, this paper proposes a dynamic asymmetric leverage (DAL) model that accommodates the direct correlation as well as the sign and magnitude of the threshold effects. A special case of the DAL model with zero direct correlation between the innovations is the asymmetric leverage (AL) model. The dynamic asymmetric leverage models are estimated by the Monte Carlo likelihood (MCL) method. Monte Carlo experiments are presented to examine the finite sample properties of the estimator. For a sample size of T = 2000 with 500 replications, the sample means, standard deviations, and root mean squared errors of the MCL estimators indicate only a small finite sample bias. The empirical estimates for S&;P 500 and TOPIX financial returns, and USD/AUD and YEN/USD exchange rates, indicate that the DAL class, including the DL and AL models, is generally superior to threshold SV models with respect to AIC and BIC, with AL typically providing the best fit to the data.     

应用Bootstrap模拟技术的VaR计算与分析研究

比较估计VaR的方差一协方差方法、历史模拟法和MonteCarlo模拟方法，提出估计VaR的新模型和新方法。新模型把随机收益率分解成刻画收益率二阶矩特征的部分和刻画随机均值的正态部分。新方法使用Bootstrap模拟技术和MonteCarlo模拟技术计算VaR。实证分析表明，新方法计算的VaR在传统方法计算的VaR参考范围内。     

VaR估计中的概率分布设定风险与改进

 在金融风险管理中,金融风险的事先判断具有极其重要的意义,然而金融机构金融决策事前支持技术的缺陷常常被忽略,在金融投资收益率概率分布估计方法尚未建立以前,将样本数据特征纳入风险度量的计算则不失为一种改进风险判断的有效途径。本文选择度量金融风险的主流方法—VaR技术来讨论概率分布设定风险,探讨依据数据特征改进和扩展VaR计算方法,通过对Delta-正态方法与Delta-Gamma-Cornish-Fisher扩展方法估计VaR值的比较,从实证分析角度论证了扩展方法在VaR估计中的有效性与稳健性。