基于异方差极值理论的基金风险度量及绩效评价

VaR是度量金融资产面临风险大小的指标,也是基金绩效评价的依据之一。本文以ETF50和ETF180基金作为研究对象,首先利用ARMA-GARCH模型捕获金融时间序列的自相关性和条件异方差性,过滤获得近似独立同分布的残差序列,再采用极值理论对残差序列进行处理,以刻画收益序列的尾部特征,从而估计ETF50和ETF180基金的动态风险VaR值,并利用该值计算基金绩效评价指标RAROC值。实证分析表明,相比于GARCH模型,结合了异方差极值理论模型的风险度量方法能更准确预测基金所面临的市场风险,而在基金绩效评价中,ETF180的RAROC值高于ETF50,因而更值得投资。     

基于极值理论的可转债在险价值动态预测研究

在对可转换债券指数统计特性分析的基础上,针对其收益率的左偏及尖峰现象,利用极值分布GPD对其尾部有效拟合,充分考虑极端值对VaR估计的影响,并利用滑动窗口方法进行VaR动态预测。采用kupiec的失败频率检验法进行VaR样本内和样本外检验,发现基于极值理论的可转债VaR方法,其估计和动态预测结果都明显优于基于偏t分布的Risk Metrics方法的测试结果,控制风险的能力适合可转债市场的要求。     

VaR方法在国债利率风险管理中的应用

利用VaR方法对国债的利率风险进行了度量,首先验证了国债收益率序列不服从正态分布,说明不宜采用正态分布假定下的VaR计算方法计算VaR,然后采用VaR的历史模拟法对国债价格的利率风险进行了度量,最后对历史模拟法的优缺点进行了分析。     

基于极值理论的沪深股市VaR和CVaR分析

将VaR和CVaR结合起来能全面描述金融时间序列与尾部相关的风险。考虑沪深股指收益序列胖尾特性,极值理论方法能够对沪深股市VaR和CVaR进行较好估计,运用基于Boot strap和极大似然估计方法解决极值理论数据不足的缺陷,从而给出对VaR和CVaR的点估计和区间估计。     

基于极值理论的人民币汇率风险测度

为了克服传统VaR计算方法在处理厚尾分布时的缺陷,本文引入极值理论中的阈值模型建模,对人民币/欧元汇率风险进行测度,然后对使用极值理论方法计算VaR的效果作了返回测试,最后对金融机构和企业的汇率风险管理工作提出了政策建议。     

基于极值理论的金融风险研究

运用极值理论的POT模型,并结合描述金融产品收益率的尾部分布更加精确的GPD分布,计算出了基于极值理论的风险估计。与传统方法相比,极值理论方法能更好的利用已知历史数据,并能在计算高置信度VaR时克服传统方法中误差较大的缺点。     

沪深300指数风险价值分析——基于PARCH-VaR方法

由于我国的沪深300指数收益率序列呈现出三种特性:左偏、尖峰厚尾和波动聚集,PARCH模型可以更完美地刻画序列的分布特征。本文利用PARCH模型对指数收益率序列进行拟合,建立PARCH-VaR模型,用以评估中国沪深300指数的风险。研究结果表明,如果假设残差同时服从三种分布:正态分布、t分布和广义误差分布,基于广义误差分布的PARCH模型计算的VaR值最能够客观地反映中国沪深300指数的风险问题。     

基于Tarch-M模型下的VaR法与TCE法在外汇风险度量中的应用

将残差项服从t分布的门限自回归条件异方差模型应用于我国外汇风险的计量。对2005年7月21日到2008年10月31日的美元/人民币汇率日波动率进行了分析,研究发现：风险值方法（VaR法）可以比较好地度量外汇风险,但是实际风险一旦超过了风险值的估计它会失去效用,所以引入了尾条件期望（TCE法）作为风险值的补充,并证明它估计超出VaR法的风险期望值是有效的。     

基于FIGARCH-EVT模型的极端风险度量

多项实证研究表明多数金融资产的收益率序列呈现尖峰厚尾的非正态分布,波动率具有聚集性和持续性等特点,因此传统的基于正态分布的VaR和ES模型无法有效捕捉收益率序列的尾部信息,忽视了波动的时变性,从而低估金融资产存在的风险。考虑到金融资产收益序列的时变性和尖峰厚尾的特点,本文采用FIGARCH模型与极值理论中的EVT模型相结合的方法来研究我国证券市场存在的市场风险,并进行有效合理的度量。通过比较分析与返回检验发现,基于FIGARCH-EVT模型的风险度量方法要明显优于传统的纯极值理论方法和GARCH-EVT模型。     

我国铜现货风险度量研究

面对铜价的剧烈波动,有效度量铜现货风险对市场参与者而言至关重要。本文比较了受金融危机影响市场环境变化前后两个时期——震荡时期和下行时期,历史模拟法HS、正态分布ND、广义条件异方差GARCH、随机分布SV和极值分布EV五种模型对铜现货对数收益率日VaR和ES的估计效果。结果表明,投资者适宜运用GARCH模型预测铜现货市场风险波动剧烈时期的风险价值VaR,运用EV模型预测铜现货风险的尾部损失的均值。     

Hedging and value at risk

In this article, it is shown that although minimum‐variance hedging unambiguously reduces the standard deviation of portfolio returns, it can increase both left skewness and kurtosis; consequently the effectiveness of hedging in terms of value at risk (VaR) and conditional value at risk (CVaR) is uncertain. The reduction in daily standard deviation is compared with the reduction in 1‐day 99% VaR and CVaR for 20 cross‐hedged currency portfolios with the use of historical simulation. On average, minimum‐variance hedging reduces both VaR and CVaR by about 80% of the reduction in standard deviation. Also investigated, as an alternative to minimum‐variance hedging, are minimum‐VaR and minimum‐CVaR hedging strategies that minimize the historical‐simulation VaR and CVaR of the hedge portfolio, respectively. The in‐sample results suggest that in terms of VaR and CVaR reduction, minimum‐VaR and minimum‐CVaR hedging can potentially yield small but consistent improvements over minimum‐variance hedging. The out‐of‐sample results are more mixed, although there is a small improvement for minimum‐VaR hedging for the majority of the currencies considered. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:369–390, 2006     

基于非对称GARCH的铜期货市场风险度量研究

针对金融时间序列普遍存在自相关性、杠杆效应、尖峰厚尾等典型事实,运用基于杠杆效应的GARCH模型,构建铜期货市场收益序列具有典型事实的动态风险测度模型：VaR-AR（m）-EGARCH（p,q）和VaR—AR（m）-TGARCH（p,q）,再通过上海期货交易所（SHFE）铜期货对所建模型进行实证分析,并运用Back—testing中的LRT（Likelihood Ratio Test）方法,对铜期货市场风险测度模型准确性和可靠性进行实证检验。实证结果表明,基于非对称GARCH的铜期货市场动态风险度量模型,能够有效捕获铜期货市场的典型事实,同时还能够准确测度具有杠杆效应的铜期货市场的动态风险,也能加强对金融市场动态风险预测能力。     

VaR在投资组合应用中存在的缺陷与CVaR模型

VaR在投资组合应用中存在的两个缺陷:一是不满足一致性公理,二是尾部损失测量的非充分性,这些缺陷可能导致组合优化上的错误.当且仅当组合回报服从正态分布时,VaR才能应用于组合优化,这极大地限制了VaR在投资组合管理中的适用范围.本文最后介绍了CVaR模对VaR模型的改进及其在投资组合优化中的应用.     

贝叶斯下的市场风险资产损失计量

目前VaR模型是国际上通行的综合衡量和管理风险的计量技术,但当分布是非正态分布或是不连续的时候VaR就没有稳定性。在应用VaR以及CVaR的基本原理,利用具有尖峰后尾特征的对数正态分布并结合贝叶斯风险,对参数通过先验分布和后验分布进行修正得到市场风险资产CVaR以及VaR的计算公式,对其进行比较分析,将其与pareto分布下的市场风险VaR值进行比较。研究表明:对数正态分布下市场风险资产的损失率比损失率呈pareto分布的风险损失显现出更好的拟合特征,能够描述市场风险资产的随机性最大损失,以及CvaR计量下的市场风险资产损失比VaR计量下的有更好的尾部特征,更加符合风险的充分性。     

基于状态转移波动模型的金融市场动态风险测度研究

测度金融市场动态风险VaR的一个关键在于如何准确刻画金融市场收益波动率。引入马尔可夫状态转移的ARCH（Regime switching ARCH,SWARCH）模型。构建出基于状态转移波动模型的金融市场动态风险测度模型,然后运用其对中国大陆上证综指和伦敦金融时报100指数的市场风险进行测度,并运用Back-testing中的似然比率检验方法（Likelihood Ratio Test,LRT）对金融市场风险测度的准确性进行检验。实证结果表明,基于SWARCH的风险测度模型,不仅能够准确测度不同类型金融市场的动态风险,而且在测度金融市场大风险方面展现出同样具有优越的测度能力。     

Asymptotic subadditivity/superadditivity of Value-at-Risk under tail dependence

This paper presents a new method for discussing the asymptotic subadditivity/superadditivity of Value-at-Risk (VaR) for multiple risks. We consider the asymptotic subadditivity and superadditivity properties of VaR for multiple risks whose copula admits a stable tail dependence function (STDF). For the purpose, a marginal region is defined by the marginal distributions of the multiple risks, and a stochastic order named tail concave order is presented for comparing individual tail risks. We prove that asymptotic subadditivity of VaR holds when individual risks are smaller than regularly varying (RV) random variables with index −1 under the tail concave order. We also provide sufficient conditions for VaR being asymptotically superadditive. For two multiple risks sharing the same copula function and satisfying the tail concave order, a comparison result on the asymptotic subadditivity/superadditivity of VaR is given. Asymptotic diversification ratios for RV and log regularly varying (LRV) margins with specific copula structures are obtained. Empirical analysis on financial data is provided for highlighting our results.     

基于RiskMetrics模型的国债指数风险研究

RiskMetrics模型是一组被广泛应用的金融风险管理工具,其核心技术是在险价值(VaR)方法。研究这种对线性金融工具计算VaR的简单方法。其中,用指数加权移动平均方法(EWMA)来计算收益率的波动,用最小RMSE标准来确定最优衰减因子。通过对上证国债指数的实证研究,计算最优的衰减因子和国债指数的每日VaR值。从中可以看出,上证国债指数的波动性较大,并且每日VaR的预测结果与所假设的置信水平能够很好的吻合。     

Value‐at‐Risk bounds with two‐sided dependence information

Value‐at‐Risk (VaR) bounds for aggregated risks have been derived in the literature in settings where, besides the marginal distributions of the individual risk factors, one‐sided bounds for the joint distribution or the copula of the risks are available. In applications, it turns out that these improved standard bounds on VaR tend to be too wide to be relevant for practical applications, especially when the number of risk factors is large or when the dependence restriction is not strong enough. In this paper, we develop a method to compute VaR bounds when besides the marginal distributions of the risk factors, two‐sided dependence information in form of an upper and a lower bound on the copula of the risk factors is available. The method is based on a relaxation of the exact dual bounds that we derive by means of the Monge–Kantorovich transportation duality. In several applications, we illustrate that two‐sided dependence information typically leads to strongly improved bounds on the VaR of aggregations.     

The information content of implied volatility in agricultural commodity markets

In this article we compare the incremental information content of lagged implied volatility to GARCH models of conditional volatility for a collection of agricultural commodities traded on the New York Board of Trade. We also assess the relevance of the additional information provided by the implied volatility in a risk management framework. It is first shown that past squared returns only marginally improve the information content provided by the lagged implied volatility. Secondly, value‐at‐risk (VaR) models that rely exclusively on lagged implied volatility perform as well as VaR models where the conditional variance is modelled according to GARCH type processes. These results indicate that the implied volatility for options on futures contracts in agricultural commodity markets provides relevant volatility information that can be used as an input to VaR models. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:441–454, 2003