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。     

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。     

VaR模型在金融证券市场的应用

我国金融市场发展前景越来越受人瞩目,存在的风险越来越受人关注,为了测算风险,人们提出了很多方法,而VaR模型自诞生至今已经成为国外度量市场风险的主流方法.本文以深证综指数据为例,基于GARCH模型,得出VaR模型的适用性,从而VaR模型在实际的市场风险度量中起一定的指导作用.     

基于VaR的采购风险度量模型

探讨将VaR风险价值法这一新型风险管理工具引入采购风险管理之中,利用VaR方法中的RiskMetrics模型对价格收益率序列方差进行拟合,建立采购风险度量模型。     

VaR模型及其在金融风险管理中的应用

风险价值(简称VaR)是目前国际金融风险管理领域广泛使用的工具,也是度量金融风险的一种新的技术标准。本文着重介绍了VaR的概念、计算及其应用,并指出VaR模型作为衡量金融市场风险的标准在我国的应用前景。     

GARCH模型与VaR的度量研究

GARCH模型在金融资产序列波动率的模拟和金融风险VaR的度量中都有着广泛的应用。本文比较研究了RiskMetrics及GARCH族的11种模型分别在正态分布和Skewed-t分布下度量VaR值的精确程度,同时对向前一步预测的VaR值进行了失败率检测法和动态分位数测试。结果表明,Skewed-t分布较好地拟合了金融资产的厚尾特性;在不同的置信水平下,FIGARCH(BBM)、FIEGARCH及IGARCH模型预测的VaR值更加精确,其高估或低估的风险程度较轻。     

浅论公司债券流动性风险的度量及其成因

VaR模型作为风险度量和管理的工具,已经被金融机构和监管当局广泛接受。但是,由于它是一定置信度和持有期内衡量资产头寸所能遭受的最大损失,它并没有考虑到是否能够真的以所计算出的可能的最差价格将所持有的头寸变现。在实践中,两个具有相同的VaR值,但流动性高低不同的投资组合,其变现的最差价格是不同的。因而,很多学者对传统的VaR模型提出了质疑,并认为把流动性风险的度量加入到目前已非常成熟的风险管理体系VaR模型中,是金融风险管理领域的一个严峻挑战和紧随的任务。     

基于VaR的证券投资组合风险评估及管理体系

针对传统的利用方差及β系数方法来度量金融市场风险的缺陷，以及VaR在金融风险管理中的主流地位，我们设计了一套基于VaR的证券投资组合风险评估及管理体系。该体系中的风险评估指标主要包括VaR、动态VaR、边际VaR、成分VaR等。这些指标不仅可以使我们明确得到一个最大可能的整体损失，还可以了解构成组合的每一项资产及其相应调整、变化对组合整体风险的影响。实证分析表明，该评估体系不仅可以使我们全面了解投资组合的风险状况，还可以帮助我们更好地进行风险管理，改善投资组合的风险收益特征。     

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。     

国际碳交易市场的风险度量及对我国的启示——基于状态转移与极值理论的VaR比较研究

本文以欧洲气候交易所公布的CERs期货报价为研究对象,将Markov波动转移引入VaR的计算,结合极值理论,度量国际碳交易市场的系统风险。首先建立SWARCH模型与MS-GARCH模型描述价格波动的阶跃特性,直接测算动态VaR。随后采用POT模型拟合标准残差序列的右尾超门限分布,确定极值分位数,再次测算动态VaR。最后通过回测检验选取最优风险值,并由此分析了我国获批碳项目变动趋势与国际碳交易市场风险变动趋势间的关系。     

基于流动性调整CAViaR模型的风险度量方法

本文提出了流动性风险度量的一个新的方法,流动性调整的CAViaR模型。该模型能够直接反映资产流动性的变动对未来风险的影响,并在此基础上计算资产未来经过流动性调整的风险VaR,从而使投资者能够更好地管理风险,尤其是流动性风险。实证研究表明,该模型能够较好地刻画中国股市流动性风险的动态变化特征;并且发现股票流动性的大幅下降通常导致未来风险明显加大,且正向流动性下降所带来的风险往往较负向流动性要更大,因此更值得投资者关注。     

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.     

基于动态VaR模型的我国银行间拆借市场风险度量研究

本文介绍了一种基于GARCH和非参数法的动态VaR模型——L_VaR模型,用来度量市场风险与流动性风险两者综合风险的大小。并通过采样我国银行间隔夜拆借的高频交易数据,以及SAS软件的数据处理分析发现,GARCH（1,1）模型能较好地拟合隔夜拆借利率的波动情况,而非参数估计法（Boot- strap）能较准确地估计拆借市场流动性的波动水平。实证结果表明。基于动态VaR模型对于市场风险与流动性风险两者综合风险的短期预测效果较为理想。     

Interest rate risk propagation: Evidence from the credit crunch

During the 2007–2009 financial crisis, US subprime mortgage risk exposures led to severe liquidity problems in several other foreign markets. Such risk contagion was caused by enormous changes in interest rates. Although risk contagion has been investigated by several literatures, the magnitude of propagated interest rate risk around global financial markets remains unexplored. Therefore, this study quantifies the degree to which the increased credit risk within the US financial system propagated to the European markets’ liquidity risks. Specifically, using a conditional value-at-risk (CoVaR) model, we quantitatively measure interest rate risk of a European country, by looking at the upside risk in distribution of changes in interest rate. And such propagation risk measure considers additional value-at-risk conditional on the interest rate movements in the US. The results show significantly positive differences between European country's value-at-risk conditional on the US financial markets being in a normal or distressed state. This propagating effect increased from 2007, and was particularly pronounced in the 2008–2009. In addition, the interest rate risk contagion is especially severe for some countries in the Euro regions with greater sovereign debt problems. Hence our result foretells the deterioration of the European sovereign debt crisis which started to unfold in 2010. Our work supplements the literature by successfully quantifying the magnitude of additional interest rate risk conditional on risk exposure from external sectors.     

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。     

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.     

金融机构高负债率下的资产管理:收益与风险的权衡

负债是金融机构经营的主要资金来源,偏高的资产负债率在财务杠杆、流动性等方面形成金融行业的显著风险,金融机构因此要借助有效的资产管理来实现自身盈利和风险控制。本文分析金融机构高负债风险的成因,提出改善盈利能力、流动性及资产配置等措施,降低负债风险,以实现价值最大化的目标。     

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。     

Funding liquidity risk and the low-volatility anomaly: Evidence from the Taiwan stock market

We investigate whether the funding liquidity risk to institutional investors influences the negative relation between expected returns and variance (the ‘‘Low-volatility anomaly’’). With the Taiwan stock market as a setting, we implement a multivariate Markov switching model and use the funding liquidity risk to model the time-varying transition probabilities of the regime-switching process to capture changes in the funding liquidity risk regime. Our evidence documents that the low-volatility anomaly is most pronounced when there is high funding liquidity risk. When there is low funding liquidity risk, however, the low-volatility anomaly has a significant reversal. These results imply that the increased funding liquidity risk due to financial shock transmitted from parent banks is associated with higher selling pressure on institutional investors’ high-volatility stocks, leading to the low-volatility anomaly.     

GARCH模型族在沪深300中的比较研究

风险测量一直是金融研究领域的热门话题,而如何构建合适的模型来衡量风险自然而然成为众多学者研究的关注点.VaR方法是当今应用最广泛的衡量金融风险的方法之一,其核心又在构建良好的波动率估计模型.GARCH模型族能很好地描述股指波动率呈现的重尾、波动性聚集、杠杆效用等,是当前效果比较好的条件异方差性的模型.本文着重研究基于GARCH模型族(GARCH、EGARCH、PGARCH)在不同分布假定下(高斯分布、t分布、广义误差分布)的表现,从而计算出沪深300的在险价值( VaR),比较分析模型拟合效果,选出适合的模型,对规范国内沪深300的风险管理提供了理论依据.