VaR方法在金融企业投资绩效评估中的应用

本文介绍了风险值(VaR)的定义与VaR的计算原理,探讨了VaR模型在金融企业投资绩效评估中的应用,引出经济资本、风险调整资本的资本收益率等指标,并对VaR方法进行了延伸。     

VaR在商业银行风险管理中的应用

VaR作为现代银行风险管理的标准和理论基础,已在国际银行中得到广泛的应用。在新巴塞尔协议中则进一步主张用VaR对市场风险、信用风险和操作风险进行综合的风险管理,本文从三个方面分析了VaR在商业银行风险管理中的应用,并相应提出了VaR应用的不足和改进措施。     

基于VaR模型对房地产风险价值评估

VaR模型是JP.Morgan公司用来计量市场风险的产物,它是一种能测量不同交易、不同业务部门市场风险,并将这些风险体现为一个数值的方法。在投资过程中人们可以应用VaR对投资对象进行风险测量,使人们根据风险的大小来决定投资的策略。本文对VaR模型进行改良,从风险因素影响房地产价格的角度,测算多种风险对投资房地产带来的最大损失。     

VaR与商业银行风险管理

VaR作为现代银行风险管理的标准和理论基础,已在国际活跃银行得到广泛的应用。在2004年通过的新巴塞尔协议则进一步主张用VaR对市场风险、信用风险和操作风险进行综合的风险管理,文章从四个方面分析了VaR在商业银行风险管理中的应用,并进一步分析了VaR应用中的局限性和补充措施。     

证券组合投资中的风险价值VaR概述

VaR作为金融风险测度的主流方法,已在证券投资领域中得到了广泛的应用。本文主要说明VaR的定义、参数和计算方法,并介绍了基于VaR风险测度的组合投资模型“均值-VaR模型”,最后概述了VaR在金融界监管和风险控制等方面的应用。     

VaR在商业银行风险管理中的应用

VaR作为现代银行风险管理的标准和理论基础,已在国际银行中得到广泛的应用.在新巴塞尔协议中则进一步主张用VaR对市场风险、信用风险和操作风险进行综合的风险管理,本文从三个方面分析了VaR在商业银行风险管理中的应用,并相应提出了VaR应用的不足和改进措施.     

VaR的产生、发展及其在中国股市中的应用

金融市场风险对任何一个投资者和监管者来说,都是高度关注和竭力避免的一个至关生存和发展的大事.文章深入浅出的阐述一种在复杂金融资产组合中能全面衡量市场风险的方法,即VaR.并结合我国股市走势,应用VaR计算实施金融市场的风险管理.     

VaR模型中流动性风险的度量

虽然传统的VaR模型已经被广泛应用于度量价格风险和信用风险，但是对流动性风险度量的考虑不多。本文归纳总结了关于如何把流动性风险度量纳入VaR模型已有的研究成果，并结合中国股票市场的实际特点，建立了一个对流动性风险进行调整的VaR模型，用以度量中国股票的价格风险和流动性风险。本文还以沪深两市的A股为样本进行了实证分析，结果证明考虑了流动性风险后，流动性差的股票其风险价值(VaR)显著大于传统的VaR模型测算的风险价值。     

基于VaR模型的证券投资风险分析

VaR(ValueatRisk)方法是分析证券投资风险的常用方法。本文介绍了VaR模型的一种分析及计算方法,即方差—协方差法,并通过介绍如何利用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。     

VaR方法与资产组合分析

本文针对风险方差度量方法投资收益正态分布假设的缺陷，引入了考察投资绩效对资产组合影响的VaR方法，在探讨VaR定义以及计算方法的基础上，求解了VaR约束下的资产组合问题。在VaR框架下，建立了形同于Sharpe指数的单位风险超额收益指数，并提出了类似于均值－方差分析中存在无风险资产的两基金分离定理，从而弥补了方差度量方法的不足，提高了资产配置模型的应用效率。     

Impact of value-at-risk models on market stability

Financial institutions around the world use value-at-risk (VaR) models to manage their market risk and calculate their capital requirements under Basel Accords. VaR models, as any other risk management system, are meant to keep financial institutions out of trouble by, among other things, guiding investment decisions within established risk limits so that the viability of a business is not put unduly at risk in a sharp market downturn. However, some researchers have warned that the widespread use of VaR models creates negative externalities in financial markets, as it can feed market instability and result in what has been called endogenous risk, that is, risk caused and amplified by the system itself, rather than being the result of an exogenous shock. This paper aims at analyzing the potential of VaR systems to amplify market disturbances with an agent-based model of fundamentalist and technical traders which manage their risk with a simple VaR model and must reduce their positions when the risk of their portfolio goes above a given threshold. We analyse the impact of the widespread use of VaR systems on different financial instability indicators and confirm that VaR models may induce a particular price dynamics that rises market volatility. These dynamics, which we have called `VaR cycles’, take place when a sufficient number of traders reach their VaR limit and are forced to simultaneously reduce their portfolio; the reductions cause a sudden price movement, raise volatility and force even more traders to liquidate part of their positions. The model shows that market is more prone to suffer VaR cycles when investors use a short-term horizon to calculate asset volatility or a not-too-extreme value for their risk threshold.     

三种Copula-VaR计算方法与传统VaR方法的比较

金融风险测量VaR方法广泛应用于银行等金融机构,Copula技术以其处理非正态联合分布函数所具有的良好性质逐渐成为国内外研究的热点。本文将Copula理论应用于VaR的计算方法,并与传统的VaR方法进行比较,通过美元和欧元组合的实证研究,得到基于Copula的VaR方法能够更加有效地测量风险的结论。     

从一致性公理看金融风险度量技术新发展

"一致性公理"用四个性质给出了好的度量模型标准。然而作为风险价值度量的VaR模型却很可能不满足"一致性公理",因此存在度量风险的缺陷。CVaR模型的提出,弥补了VaR模型不满足次可加性以及尾部风险度量不充分的两大缺陷。于是在传统的VaR方法基础上,融合进市场风险和流动性风险,形成一些新的金融衍生工具的风险管理框架。     

在险价值(VaR)计量方法的解析与研究

在险价值是近年来发展起来的一种用于量化和控制金融风险的模型。本文在介绍在险价值基本思想的基础上,对常用的四种在险价值计量方法进行逐一介绍,并分析每种方法的优点和局限性;最后,对在险价值及计量方法进行总的评述。     

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.     

基于信用度量制模型的互换价值VaR的计量

互换这一衍生工具有助于规避风险、谋取收益。但互换本身也有金融风险。由于涉及两个或两个以上交易主体,信用风险是互换交易中的主要金融风险形式之一。本文运用信用度量制模型度量了互换价值VaR,以期对我国金融机构严重信用风险的度量、管理有所借鉴。     

Has the Basel Accord improved risk management during the global financial crisis?

The Basel II Accord requires that banks and other Authorized Deposit-taking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure Value-at-Risk (VaR). The risk estimates of these models are used to determine capital requirements and associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. In this paper we define risk management in terms of choosing from a variety of risk models, and discuss the selection of optimal risk models. A new approach to model selection for predicting VaR is proposed, consisting of combining alternative risk models, and we compare conservative and aggressive strategies for choosing between VaR models. We then examine how different risk management strategies performed during the 2008–09 global financial crisis. These issues are illustrated using Standard and Poor's 500 Composite Index.     

基于极值分布理论的VaR与ES度量

本文应用极值分布理论对金融收益序列的尾部进行估计,计算收益序列的在险价值VaR和预期不足ES来度量市场风险。通过伪最大似然估计方法估计的GARCH模型对收益数据进行拟合,应用极值理论中的GPD对新息分布的尾部建模,得到了基于尾部估计产生收益序列的VaR和ES值。采用上证指数日对数收益数据为样本,得到了度量条件极值和无条件极值下VaR和ES的结果。实证研究表明:在置信水平很高(如99%)的条件下,采用极值方法度量风险值效果更好。而置信水平在95%下,其他方法和极值方法结合效果会很好。用ES度量风险能够使我们了解不利情况发生时风险的可能情况。     

VaR方法对我国保险投资风险管理的借鉴及应用

近20多年来金融市场迅猛发展,金融机构面临的主要风险已从信用风险转向了市场风险。我国金融市场作为一个发展中的新兴市场,市场风险必将随着金融市场的发展而逐渐加大。保险公司作为经营风险的企业,必将面临着这种金融风险。研究VaR对于保险投资风险管理的影响可以防患于未然,有效地避免由于信用风险和市场风险带来的损失。     

VaR方法在风险评估中的应用

现代金融市场的金融产品种类繁多,市场不确定因素不断加大,市场风险越来越大,传统的风险评估方法难以满足市场对风险评估的需要。VaR法通过定量计算风险值,以求达到对风险准确评估的目的。