基于VaR模型的证券投资组合风险分析

VaR方法是分析证券投资风险的常用方法,本文介绍VaR模型的一种分析及计算方法,即蒙特卡洛模拟法.通过介绍如何利用VaR模型理论分析我国证券市场中存在的投资风险,为我国投资者进行投资提供参考.     

如何用CVaR模型监测极端风险

金融活动在很大程度上是风险管理活动,一次次的灾难性事件提示着我们风险管理的重要性。本文通过实证检验,发现CVaR模型对历史上海外几次大级别股灾都有显著警示作用,同时计算了1999年至今的沪深指数VaR与CVaR时序,以及2006年至今的模拟股票投资组合VaR与CVaR时序,发现CVaR指标对极端事件的发生更为敏感,并且与VaR模型结合使用会起到更好的风险识别作用,从而及早提示黑天鹅的到来。     

回测检验在商业银行市场风险度量中的应用研究

不同内部模型计量的VaR值往往差异很大,回测检验已成为商业银行选择、改进和评价内部模型时不可或缺的重要工具。本文对多种回测检验工具进行了实证研究,并对不同类型回测检验工具的优缺点进行了评价,提出通过两步法建立内部模型回测检验系统的政策建议。     

CVaR HEDGING USING QUANTIZATION‐BASED STOCHASTIC APPROXIMATION ALGORITHM

In this paper, we investigate a method based on risk minimization to hedge observable but nontradable source of risk on financial or energy markets. The optimal portfolio strategy is obtained by minimizing dynamically the conditional value‐at‐risk (CVaR) using three main tools: a stochastic approximation algorithm, optimal quantization, and variance reduction techniques (importance sampling and linear control variable), as the quantities of interest are naturally related to rare events. As a first step, we investigate the problem of CVaR regression, which corresponds to a static portfolio strategy where the number of units of each tradable assets is fixed at time 0 and remains unchanged till maturity. We devise a stochastic approximation algorithm and study its a.s. convergence and weak convergence rate. Then, we extend our approach to the dynamic case under the assumption that the process modeling the nontradable source of risk and financial assets prices is Markovian. Finally, we illustrate our approach by considering several portfolios in connection with energy markets.     

我国国际储备资产的最优结构研究——基于时变Copula及VaR的投资组合模型分析

文章通过构建效用评估函数,使用时变相关T-Copula模型、Monte Carlo模拟和VaR计算方法系统研究了我国国际储备的最优结构。结果发现,我国黄金的最优占比应至少为23%。据此,文章对多个国家储备资产的变化情况进行了动态和静态比较,发现不同类别的演化路径,而以我国和其他金砖四国为代表的类别处于收益递减、风险增大的状态中,亟须加大黄金储备至优化区间。     

金融资产综合风险价值动态评估研究

为综合度量金融资产损失的市场风险与流动性风险,采用GARCH-VaR模型度量了日市场风险价值,用日内相对波动幅度调整为日LA-VaR,并利用时间延展槡T规则将它转换为变现期间的综合风险价值,构建了金融资产综合风险价值的全方位动态评估模型。通过以中国股指期货为例的实证研究证明,该模型能够有效评估金融资产综合风险价值,适用于金融资产公允价值的期末估算。     

基于边际效应的银行个体系统性风险贡献测度研究

本文使用系统性风险β值法度量我国上市银行的系统性风险以及上市银行对系统性风险的贡献度。研究结果表明,单个机构对系统性风险的贡献不仅取决于系统性风险β值,还受到其个体风险值的影响。总体而言,国有大型商业银行的系统性风险β值高于中小股份制商业银行,对系统性风险的边际贡献和影响也较大。但中小股份制商业银行抵御风险的能力相对较弱,尽管β值较小,一旦个体风险值急剧增加,其对系统性风险的影响也可能超过国有大型商业银行。因此,系统性风险的防范既要关注那些系统性风险β值大的银行,也要关注个体风险值可能出现剧烈波动的中小银行。     

Value-at-risk in a market subject to regime switching

Many empirical researches report that value-at-risk (VaR) measures understate the actual 1% quantile, while for Inui, K., Kijima, M. and Kitano, A., VaR is subject to a significant positive bias. Stat. Probab. Lett., 2005, 72, 299–311. proved that VaR measures overstate significantly when historical simulation VaR is applied to fat-tail distributions. This paper resolves the puzzle by developing a regime switching model to estimate portfolio VaR. It is shown that our model is able to correct the underestimation problem of risk.     

A simple two-component model for the distribution of intraday returns

We model the conditional distribution of high-frequency financial returns by means of a two-component quantile regression model. Using three years of 30 minute returns, we show that the conditional distribution depends on past returns and on the time of the day. Two practical applications illustrate the usefulness of the model. First, we provide quantile-based measures of conditional volatility, asymmetry and kurtosis that do not depend on the existence of moments. We find seasonal patterns and time dependencies beyond volatility. Second, we estimate and forecast intraday Value at Risk. The two-component model is able to provide good-risk assessments and to outperform GARCH-based Value at Risk evaluations.     

EWMA-GARCH模型与GARCH模型在估计收益率波动上的差异的实证及理论分析

VaR作为衡量风险的指标,其核心则在于对波动,亦即方差的估计。基于时间序列,关于条件方差的经典模型是GARCH模型,尽管后来又衍生出了EGARCH,PARCH等复杂模型,但在实务中GARCH模型仍占有重要的地位。文章分析了一种比较新的结合了EWMA模型的GARCH模型(以下称为EWMA-GARCH模型)计算VaR的参数估计方法,以检验其在估计波动上的实用性,并对实证检验结果做了理论分析。分析结果表明,尽管该结合模型缺乏完整的理论支持,但是其计算效果仍比较良好,当然这样良好的结果是建立在因缺乏理论依据而导致的对模型的其他要求之上的.至于是采用受理论支持的模型还是并不输实践价值的模型,文章也给出了一定的建议。