基于VaR模型的最优套期保值比例探析——以美元远期套保为例

VaR模型被认为是兼顾投机套利与套期保值两大动机的衍生工具决策模型。国内文献对此讨论较少。基于其理论推导,可以归纳地认为VaR模型具有兼容性、一般性、"期权"特征和可操作性。以美元远期套保为例,研究发现,基于VaR模型的最优套保比较之传统最优套保比、最小方差最优套保比更有优势,能够解释中国企业运用衍生工具失败的原因。但据此推论,基于VaR模型确定最优套保比在实践中仍然存在一些问题,如非正态分布、现货头寸不确定、损益报告困难等。     

金融风险管理的VaR方法及其应用

近年来,巴林银行、长期资本管理公司等一系列因承担市场风险而发生巨额损失甚至倒闭的案例,使得无论金融机构还是监管当局都日益重视对市场风险的管理。与传统风险测量技术相比,VaR模型体现了更大地适应性和科学性,并且已得到大多数金融机构的认可。文章主要对VaR模型的概念、VaR模型的计算方法和VaR模型的应用进行了介绍,并借助于Excel、Matlab、SAS等软件,通过实例,详细介绍了VaR模型的两种计算方法,即历史模拟法和蒙特卡罗模拟法;另外,也借助该实例,阐述了VaR模型在风险度量和业绩评价中的实际运用。     

金融风险管理的VaR方法及其应用

近年来,巴林银行、长期资本管理公司等一系列因承担市场风险而发生巨额损失甚至倒闭的案例,使得无论金融机构还是监管当局都日益重视对市场风险的管理。与传统风险测量技术相比,VaR模型体现了更大地适应性和科学性,并且已得到大多数金融机构的认可。文章主要对VaR模型的概念、VaR模型的计算方法和VaR模型的应用进行了介绍,并借助于Excel、Matlab、SAS等软件,通过实例,详细介绍了VaR模型的两种计算方法,即历史模拟法和蒙特卡罗模拟法;另外,也借助该实例,阐述了VaR模型在风险度量和业绩评价中的实际运用。     

电力企业融资风险评价

本文运用VaR(Value at Risk)方法对风险进行评价,然后结合模糊综合评价法建立了基于VaR的风险评价模型,最后以大唐微水发电厂扩建工程为例进行风险评价,验证了该模型的可行性。     

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。     

我国开放式基金业绩评价的实证研究——基于VaR的业绩评价方法与三大经典评价方法的比较分析

现代投资理论认为,只有经风险调整后的收益才可以作为相互比较的基础来评价投资基金的投资业绩.因此,基金业绩评价的核心应该是对其所面临的风险进行准确的计量.通过对我国开放式基金应用基于VaR的评价方法与三大经典评价方法进行业绩评价的比较分析发现,从投资者注重下跌风险的角度来看,基于VaR的基金业绩评价指标相对于三大经典指标有较明显的应用优势,投资者又多一种能符合自己风险偏好的评价指标作为投资参考.     

VaR的原理及其在可转债的应用

目前,金融资产市场风险的通用度量工具为VaR(Value at Risk)模型("风险估值"模型).在巴塞尔协议形成后,用VaR度量金融风险更是受到普遍关注.本文利用期权定价公式中期权与标的资产的关系,将VaR的概念应用到可转债.本文以招行转债为例运用Matlab7.0进行Monte carlo模拟,并计算得出其VaR.     

外汇市场的VaR风险度量

目前,金融资产市场风险的通用度量工具为VaR(Value at Risk)模型("风险估值"模型),在几个巴塞尔协议形成后,用VaR度量金融风险更是受到普遍关注。文章试以外汇市场的风险度量为研究对象,收集近三年来的外汇交易收盘价,使用VaR模型,论证利用VaR技术计算外汇风险的可行性。     

商业银行利率风险管理浅析

利率的市场化导致商业银行利率风险凸现,本文分析了目前商业银行采用的四种利率风险管理方法:缺口管理,久期管理,VaR方法和利用金融衍生工具法,认为我国金融市场发展现状下,衍生工具大量缺失,银行的利率风险管理应该由主要采用缺口管理改为主要运用久期管理,并在信息技术支持下,研究和使用VaR方法,对利率风险进行动态、全面的监管,同时对衍生工具进行研究,在人才和技术等方面进行准备,最终实现利用衍生工具这一方便、灵活又成本低廉的工具对利率风险进行有效管理的目标。     

基于VaR的开放式股票型基金市场风险的测量与评价

通过采用半参数法计算投资组合VaR,得到相应VaR的近似置信区间,并结合成分VaR、边际VaR对投资组合vaR进行分解,结果发现,VaR作为风险管理工具同样可以有效应用于开放式股票型基金市场风险的测量与评价.     

基于GARCH族模型的黄金市场的风险度量与预测研究

本文以上海和伦敦黄金市场的现货交易为对象,比较研究了不同分布假定下RiskMetrics、GARCH族及其衍生模型度量VaR值的精确程度,并对超前一天预测的VaR值进行了失败率检测和动态分位数测试。结果表明:两个市场的收益率分布均具有尖峰厚尾、波动集聚和长记忆性等特征;学生t分布很好的刻画了上海黄金市场的风险特征,而正态分布则适合描述伦敦黄金市场特征;上海黄金市场相比于伦敦黄金市场风险更大。     

Alternative statistical distributions for estimating value-at-risk: theory and evidence

A number of applications presume that asset returns are normally distributed, even though they are widely known to be skewed leptokurtic and fat-tailed and excess kurtosis. This leads to the underestimation or overestimation of the true value-at-risk (VaR). This study utilizes a composite trapezoid rule, a numerical integral method, for estimating quantiles on the skewed generalized t distribution (SGT) which permits returns innovation to flexibly treat skewness, leptokurtosis and fat tails. Daily spot prices of the thirteen stock indices in North America, Europe and Asia provide data for examining the one-day-ahead VaR forecasting performance of the GARCH model with normal, student??s t and SGT distributions. Empirical results indicate that the SGT provides a good fit to the empirical distribution of the log-returns followed by student??s t and normal distributions. Moreover, for all confidence levels, all models tend to underestimate real market risk. Furthermore, the GARCH-based model, with SGT distributional setting, generates the most conservative VaR forecasts followed by student??s t and normal distributions for a long position. Consequently, it appears reasonable to conclude that, from the viewpoint of accuracy, the influence of both skewness and fat-tails effects (SGT) is more important than only the effect of fat-tails (student??s t) on VaR estimates in stock markets for a long position.     

Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach

We propose a method for estimating Value at Risk (VaR) and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudo-maximum-likelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model. We use our method to estimate conditional quantiles (VaR) and conditional expected shortfalls (the expected size of a return exceeding VaR), this being an alternative measure of tail risk with better theoretical properties than the quantile. Using backtesting of historical daily return series we show that our procedure gives better 1-day estimates than methods which ignore the heavy tails of the innovations or the stochastic nature of the volatility. With the help of our fitted models we adopt a Monte Carlo approach to estimating the conditional quantiles of returns over multiple-day horizons and find that this outperforms the simple square-root-of-time scaling method.     

On Bayesian Value at Risk: From Linear to Non-Linear Portfolios

This paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of linear portfolios. By imposing the conjugate-prior assumptions, a closed-form expression for the Bayesian VaR is obtained. The Bayesian VaR model can also be adjusted in order to deal with the ageing effect of the past data. By adopting Gerber-Shiu's option-pricing model, our Bayesian VaR model can also be applied to deal with non-linear portfolios of derivatives. We obtain an exact formula for the Bayesian VaR in the case of a single European call option. We adopt the method of back-testing to compare the non-adjusted and adjusted Bayesian VaR models with their corresponding classical counterparts in both linear and non-linear cases.     

VaR模型及其在寿险公司风险管理中的应用

随着保险产品的创新、竞争的加剧及金融市场的发展,我国寿险公司面临的风险将更为复杂和多样,如何对这些风险进行有效管理是寿险公司面临的重要难题,而VaR模型作为当前国际金融风险管理和金融监管的主流方法,含有丰富的风险管理思想,故对VaR模型进行研究并探讨其在我国寿险公司风险管理中的应用具有一定的现实意义。本文分析了寿险公司应用VaR模型时需注意的问题,并指出我国寿险公司建立基于VaR的风险管理体系需在公司治理结构、风险管理组织架构、风险管理技术、风险数据库等方面加以完善。     

Learning by Failing: A Simple VaR Buffer

We study in this article the problem of model risk in VaR computations and document a procedure for correcting the bias due to specification and estimation errors. This practical method consists of “learning from model mistakes”, since it dynamically relies on an adjustment of the VaR estimates – based on a back‐testing framework – such as the frequency of past VaR exceptions always matches the expected probability. We finally show that integrating the model risk into the VaR computations implies a substantial minimum correction to the order of 10–40% of VaR levels.     

巨灾风险厚尾分布：POT模型及其应用

针对Gamma,LognormM和Weibull等传统厚尾分布拟合巨灾风险的不足,本文一方面从理论上分析探讨了POT模型及其拟合巨灾厚尾风险的相对优势,另一方面应用POT模型和GPD分布,对我国1952年～2008年间地震直接经济损失数据进行拟合,发现了POT模型拟合巨灾风险厚尾部分的效果比Gamma、LognormM...     

Value-at-Risk: a multivariate switching regime approach

This paper analyses the application of a switching volatility model to forecast the distribution of returns and to estimate the Value-at-Risk (VaR) of both single assets and portfolios. We calculate the VaR value for 10 Italian stocks and a number of portfolios based on these stocks. The calculated VaR values are also compared with the variance–covariance approach used by JP Morgan in RiskMetrics™ and GARCH(1,1) models. Under backtesting, the VaR values calculated using the switching regime beta model are preferred to both other methods. The Proportion of Failure and Time Until First Failure tests [The Journal of Derivatives (1995) 73–84] confirm this result.     

互联网金融资产的多目标投资组合研究

互联网金融的快速发展和不断创新,正在悄然改变着公众的投资理财行为。本文在分析互联网金融创新下公众流动性偏好、投资行为变化与资产选择的基础上,构建基于CRRA(常数相对风险厌恶)期末财富期望效用最大化和VaR最小化的多目标投资组合模型。同时引入多目标优化的NSGA-Ⅱ遗传算法,并选择实际数据对模型进行求解,得出最优的互联网金融资产组合。研究表明:(1)互联网金融给传统金融业带来冲击的同时,也改变了人们的流动性偏好、投资行为和资产组合选择。(2)互联网金融在一定程度上调和了金融资产“流动性、收益性和安全性”之间的矛盾,并兼顾了“三性”的相对统一。(3)模型求解结果显示,投资者对互联网金融资产的投资组合为低风险类资产60%左右、高风险类资产40%左右。