基于条件VaR(CVaR)的投资组合优化模型及比较研究

在总结投资组合优化模型的基础上 ,详细分析了 Va R和 CVa R的概念及重要性质 ,分析了将 Va R和 CVa R应用于决策问题的处理技巧 .针对传统均值—方差模型 ,提出不考虑各种限定条件的基于条件Va R( CVa R)的投资组合优化模型以及考虑限定条件的一般模型 .针对模型的适用性 ,详细比较了 CVa R模型和均值—方差模型的应用复杂性 ,并利用实际数据进行了对比 .     

VaR方法在证券市场尖峰、胖尾分布中的实证分析

标准 Va R方法的假设条件与实际数据之间存在较大的差距 ,而差距是必严重影响 Va R方法的实际应用效果 .为此 ,本文从实际数据的基本特征出发 ,讨论了 Va R方法在尖峰、胖尾分布中的计算公式 ,并使用该计算公式对我国证券市场的实际数据进行了实证分析 .分析结果表明 ,推广的 Va R计算方法对证券市场风险预警有更可靠的揭示作用 .     

投资机会与VaR约束下的投资组合分析

在证券收益率服从正态分布的前提下 ,建立了投资机会与 Va R双重约束下的投资组合模型 ,讨论了最优解的存在唯一性 ,并得到了最优解的解析表达式 .     

多损失条件风险值的多层规划模型

对于多个损失函数,在给定的置信水平下,首先定义了不超过给定损失值的最小风险值(即Va R值)和基于权值的累积期望损失值(即CVa R损失值)概念,然后建立了一个多损失条件风险值的多层规划模型.该模型的目标是求各层多损失CVa R值达最小的最优策略,并证明了它等价于另一个较容易求解的多层规划模型.最后,给出了三级供应链中多产品的定价与订购的条件风险值模型(三层线性规划模型).     

基于SGT分布的ES估计、后验分析及在沪深股市中应用

由于VaR可能低估尾部风险,巴塞尔委员会在第三次巴塞尔协议~([1])中建议将ES取代VaR作为主要的风险度量工具,因此,有必要提出更精确且稳健的ES估计模型。鉴于股票收益率序列通常同时呈现出尖峰、厚尾、偏斜等特征,为更全面地刻画这些特征,本文采用具有三个形状参数的广义偏t分布(Skewed Generalized T Distribution,SGT)刻画收益率序列的分布形状,该分布囊括了多种常见的主流分布,通过结合能够刻画收益率序列杠杆性的EGARCH模型来估计收益率序列的ES,然后使用Du和Escanciano~([2])最近提出的ES后验分析方法对其稳健性进行评估。在实证研究中,本文将该模型用于估计我国上证综指和深圳成指的日ES,结果表明,本文提出的EGARCH-SGT模型相比常见的基于偏t分布和学生t分布的EGARCH模型明显呈现出对收益率序列更好的拟合效果,且基于该模型估计的ES顺利通过了后验分析,表现出较好的稳健性。     

内积关系与线性变换再探讨

关于欧氏空间V的变换σ满足适当条件时,成为V的线性变换的问题,陈成存[1]和王新哲[2]已做过一些讨论．本文之目的在于建立两个能够包含文[1]与文[2]所有已知结果的统一结论,从而使得对此问题的把握达到一个新的层次．定理1设σ、r、p为欧氏空间V的三个变换,如果对任意a,qeV都有。（a）,。（用）一则p都是V的线性变换．证明"．"Va,肌V,Va,hER都有．"．a为V的线性变换．下证产为V的线性变换．由条件可得：Va,qeV都有．"．Va,p,y6V,Va,b6R都有．"．尸为V的线性变换．完全类似可证：。也是V的线性变换．定义1设a为n（＞0…     

基于因子隐马尔可夫模型的VaR与ES联合预测

VaR和ES是衡量金融资产风险的重要测度,对风险控制和金融危机的识别具有重要意义。本文以CAViaR模型为基础,通过因子隐马尔可夫模型构造潜变量,作为CAViaR模型的回归系数的组成部分,最终提出了一个含潜变量的VaR和ES联合估计方法(FHM-CAViaR),实现了VaR和ES的联合预测。在该模型中,潜变量由一个因子隐马尔可夫模型驱动,可以刻画市场信息对模型系数带来的长期效应与短期冲击,该因子隐马尔可夫模型的引入实现了分位数回归模型参数在上百个状态间的转换。最后,基于本文提出的FHM-CAViaR模型分别对上证综指、深证综指和纳斯达克指数的对数收益率数据进行实证分析。实证结果表明,本文提出的模型具有更优的预测效果。此外实证结果还表明,在危机期间VaR的序列聚集性有着显著的增加。本文提出的模型可以通过潜变量的变化识别市场的机制变换,且能更精确地对金融资产的VaR以及ES进行估计,给出金融风险度量一种新的研究方法。     

基于GARCH模型的期望损失ES非参数核估计

风险度量ES最新的非参数估计方法,不依赖于分布假设,但不能动态反应金融时间序列的风险.针对金融时间序列的波动,结合GARCH模型进行期望损失ES的非参数核估计,得到随市场波动而动态变化的ES预测.通过数值模拟和对近两年的上证指数实证分析验证了该方法能准确而有效的反映市场风险.     

风险度量ES半参数模型估计及其应用

预期不足或称期损(Expected Shortfall,ES)是近几年发展起来的重要风险度量工具,对其进行建模和估计是统计学和金融计量经济学研究的前沿问题之一.本文基于平均剩余寿命模型提出一种ES估计的半参数模型,并使用广义估计方程(GEE)的方法估计参数.同时建立了严平稳α混合相依序列下参数估计的大样本理论.本文模型的意义在于可以研究资产组合的风险来源以及各风险因素对ES大小的影响程度.最后,将本文的模型应用到金融股票市场的风险评估中,结果表明此模型可以对某些金融市场现象作出合理的解释,是一个灵活且合理的金融计量统计模型.     

POTTS模型的临界失真和下界估计

证明了格上q一元Potts模型的临界失真dc和该模型Mayer级数的收敛半径R有以下关系：dc=（q－1）R／（1十R）．利用级数的矩阵表示可迭代地估算R和dc．对那些Mayer级数尚未知的模型,给出了dc下界的一个统一表示．     

Estimating Value-at-Risk and Expected Shortfall using the intraday low and range data

Value-at-Risk (VaR) is a popular measure of market risk. To convey information regarding potential exceedances beyond the VaR, Expected Shortfall (ES) has become the risk measure for trading book bank regulation. However, the estimation of VaR and ES is challenging, as it requires the estimation of the tail behaviour of daily returns. In this paper, we take advantage of recent research that develops joint scoring functions for VaR and ES. Using these functions, we present a novel approach to estimating the two risk measures based on intraday data. We focus on the intraday range, which is the difference between the highest and lowest intraday log prices. In contrast to intraday observations, the intraday low and high are widely available for many financial assets. To alleviate the challenge of modelling extreme risk measures, we propose the use of the intraday low series. We draw on a theoretical result for Brownian motion to show that a quantile of the daily returns can be estimated as the product of a constant term and a less extreme quantile of the intraday low returns, which we define as the difference between the lowest log price of the day and the log closing price of the previous day. In view of this, we use estimates of the VaR and ES of the intraday low returns to estimate the VaR and ES of the daily returns. We provide empirical support for the new proposals using data for five stock indices and five individual stocks.     

t-分布与不同风险度量下有效投资组合及其边缘的相互关系研究

In order to study the effect of different risk measures on the efficient portfolios (frontier) while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios (frontier) based on risk measures such as variance, value at risk (VaR), and expected shortfall (ES) is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance (MV) efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.     

STUDY ON THE INTERRELATION OF EFFICIENT PORTFOLIOS AND THEIR FRONTIER UNDER t DISTRIBUTION AND VARIOUS RISK MEASURES

In order to study the effect of different risk measures on the efficient portfolios (frontier) while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivari-ate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios (frontier) based on risk measures such as variance, value at risk (VaR), and expected shortfall (ES) is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance (MV) efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.     

STUDY ON THE INTERRELATION OF EFFICIENT PORTFOLIOS AND THEIR FRONTIER UNDER t DISTRIBUTION AND VARIOUS RISK MEASUREgS

In order to study the effect of different risk measures on the efficient portfolios （fron- tier） while properly describing the characteristic of return distributions in the stock market, it is assumed in this paper that the joint return distribution of risky assets obeys the multivariate t-distribution. Under the mean-risk analysis framework, the interrelationship of efficient portfolios （frontier） based on risk measures such as variance, value at risk （VaR）, and expected shortfall （ES） is analyzed and compared. It is proved that, when there is no riskless asset in the market, the efficient frontier under VaR or ES is a subset of the mean-variance （MV） efficient frontier, and the efficient portfolios under VaR or ES are also MV efficient; when there exists a riskless asset in the market, a portfolio is MV efficient if and only if it is a VaR or ES efficient portfolio. The obtained results generalize relevant conclusions about investment theory, and can better guide investors to make their investment decision.     

基于VaR和ES调整的Sharpe比率及在基金评价中的实证研究

传统Sharpe比率将投资收益的标准差作为风险的度量,而实证研究中更关注基金的损失风险而非全部风险,这是收益标准差所无法准确刻画的。针对传统Sharpe比率的这一缺点,本文考虑了用于度量下方风险的指标风险价值VaR(Value at Risk)和预期不足ES(Expected Shortfall)来替代投资收益的标准差,从而对传统Sharpe比率进行了调整。这里对VaR和ES进行计算时,运用了经验非参数估计和非参数平滑核估计两种方法。此外,本文还考虑了基金收益随时间波动的动态性,用广义自回归异方差GARCH模型对收益波动进行模拟,考察动态的VaR和ES,在实践中以动态的VaR和ES评价风险收益更加灵活。在实证研究中,本文用传统的Sharpe比率、基于VaR和ES的Sharpe比率以及基于条件VaR和条件ES的条件Sharpe比率对国内证券市场上所有26只封闭式基金在2005-2009年间的业绩进行了实证分析,分析了基金在不同指标下所体现的风险控制能力和收益水平的差别,并基于不同指标对所有基金进行了排名。此外,本文还运用协整检验考察基金收益率与市场基准指数是否存在联动关系,检验证明两者并不存在长期的均衡关系。     

基于混合模型对地震巨灾风险的分析

POT模型常被用于分析巨灾风险,然而在应用POT模型时,阀值的估计及选择存在很多困难。本文提出用混合模型对巨灾风险进行估计,并讨论混合模型的贝叶斯统计分析。基于混合模型及贝叶斯统计方法,本文对我国1966年至2014年问GDP调整后的地震直接经济损失进行分析,并根据最终模型计算出不同置信度水平下的VaR值和ES值,为我国地震巨灾风险管理提供了理论依据。     

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。     

α-混合序列下期望损失ES的两步核估计

期望损失(Expected Shortfall,ES)是当今最流行的金融资产风险管理的工具之一,是一个理想的一致性风险度量.本文在α-混合序列具有幂衰减混合系数条件下,用两步核估计估算风险度量ES的值,第一步是在险价值(Value at Risk,VaR)的核估计,第二步是ES的核估计.得到ES的核估计量的Bahadur表示,以及均方误差和渐近正态性的收敛速度.     

A New Robust Risk Measure: Quantile Shortfall

Among recent measures for risk management, value at risk (VaR) has been criticized because it is not coherent and expected shortfall (ES) has been criticized because it is not robust to outliers. Recently,[Math. Oper. Res., 38, 393-417 (2013)] proposed a risk measure called median shortfall (MS) which is distributional robust and easy to implement. In this paper, we propose a more generalized risk measure called quantile shortfall (QS) which includes MS as a special case. QS measures the conditional quantile loss of the tail risk and inherits the merits of MS. We construct an estimator of the QS and establish the asymptotic normality behavior of the estimator. Our simulation shows that the newly proposed measures compare favorably in robustness with other widely used measures such as ES and VaR.     

金融风暴中基于非参估计VaR和ES方法的风险度量

文中利用VaR和ES几个最新的非参数估计方法,对几个常见指数(上证指数、深成指数、恒生指数和道琼斯指数)及个别关系经济民生的板块(钢铁板块、房地产板块和金融板块)做实证分析,来研究金融危机对整个市场及一些重要板块的影响。实证发现:存在着金融危机爆发前风险被低估及金融危机爆发后风险得到释放的现象;用基于非参数估计的VaR和ES来度量单个指数的风险,虽然风险值存在一些差别,但判断指数风险在金融危机前后的变化,二者得出的结论是一致的。