基于金融工程的信用风险量化模型

本文首先从工程学的角度,简要分析了金融工程的原理及其应用.然后基于新巴塞尔协议,阐述了信用风险量化的基础概念:VaR(Value at Risk),并简要论述了信用风险量化的基本思路;最后,对目前广泛应用的信用风险分析模型--CreditMetrics进行了评述.     

商业银行汇率风险管理研究

本文首先引用VaR模型的方法对商业银行汇率风险进行了衡量,并根据衡量的结果来控制商业银行的汇率风险;然后对我国商业银行汇率风险管理现状进行了分析,并提出了建设性的改进策略。     

Extreme observations and risk assessment in the equity markets of MENA region: Tail measures and Value-at-Risk

The standard “delta-normal” Value-at-Risk methodology requires that the underlying returns generating distribution for the security in question is normally distributed, with moments which can be estimated using historical data and are time-invariant. However, the stylized fact that returns are fat-tailed is likely to lead to under-prediction of both the size of extreme market movements and the frequency with which they occur. In this paper, we use the extreme value theory to analyze four emerging markets belonging to the MENA region (Egypt, Jordan, Morocco, and Turkey). We focus on the tails of the unconditional distribution of returns in each market and provide estimates of their tail index behavior. In the process, we find that the returns have significantly fatter tails than the normal distribution and therefore introduce the extreme value theory. We then estimate the maximum daily loss by computing the Value-at-Risk (VaR) in each market. Consistent with the results from other developing countries [see Gencay, R. and Selcuk, F., (2004). Extreme value theory and Value-at-Risk: relative performance in emerging markets. International Journal of Forecasting, 20, 287–303; Mendes, B., (2000). Computing robust risk measures in emerging equity markets using extreme value theory. Emerging Markets Quarterly, 4, 25–41; Silva, A. and Mendes, B., (2003). Value-at-Risk and extreme returns in Asian stock markets. International Journal of Business, 8, 17–40], generally, we find that the VaR estimates based on the tail index are higher than those based on a normal distribution for all markets, and therefore a proper risk assessment should not neglect the tail behavior in these markets, since that may lead to an improper evaluation of market risk. Our results should be useful to investors, bankers, and fund managers, whose success depends on the ability to forecast stock price movements in these markets and therefore build their portfolios based on these forecasts.     

广义帕累托分布模型:风险管理的工具

在广义帕累托分布模型中,门限值过小,极限定理不成立,得到的估计是有偏的;反之,门限值过大,可以分析的数据减少,分析的偏差减少,但估计的方差增加.如何适当选取门限值是一个非常关键的问题.通过对尾部分布何时服从广义帕累托分布进行检验,可得到相应的门限值和VaR,这对金融资产管理具有重要意义.     

基于VaR和两基金分离的投资组合模型

本文使用VaR来度量投资组合的市场风险,构造了一个在可接受期末财富约束条件下,使VaR达到最小的投资组合模型,同时,发现该模型发生了两基金分离现象,因此存在多风险资产情形下的投资组合模型可以退化成为单风险资产情形下的投资组合模型。最后,本文使用简化的单风险模型对我国上海股票市场进行了实证分析,探讨投资者如何在股票和银行借贷中进行最优资产分配。     

VaR与ES在波罗的海运价指数风险测度中的应用

选取2006年1月3日至2010年12月24日波罗的海运价指数（BDI）数据进行实证分析,将基于GARCH计算的VaR和ES风险测度方法引入到BDI风险测度中,根据所得到的VaR和ES估计值定量的考察BDI的风险状况。     

我国商业银行利率风险的度量研究——以同业拆借市场为例

随着我国利率市场化改革进程的加快,我国商业银行面临的利率风险问题凸现在人们面前。本文以全国银行间同业拆借市场利率为模拟市场利率变量,以商业银行同业拆借市场日头寸为分析对象,通过定量的方法考察我国商业银行在市场波动环境下利率日风险值具体数值的大小,研究发现我国国有商业银行和农村信用社利率风险偏大,城市商业银行次之,外资银行利率风险最小。在上述实证结果基础上,文章剖析了我国商业银行利率风险管理现状,并对我国商业银行利率风险管理提出了相应的对策建议。     

金融风险计量研究——基于VaR模型的分析

本文通过分析风险的本质涵义,提出了定量分析金融风险的路径;通过回顾对于金融风险定量研究的一些模型和方法,详细介绍了VaR模型及其估计技术,指出了风险定量技术的广阔应用前景。     

基于风险价值和期权理论的Credit Metrics模型研究

Credit Metrics模型是国际金融界内流行的现代信用风险度量模型,它以风险价值VaR和期权定价思想为基础,以衡量信贷资产组合的风险价值为核心,用于识别贷款、债券等传统信贷产品的信用风险,开启了银行信用风险量化评估的先河,开创了现代信用风险度量研究的新领域,对中国商业银行的信用风险管理有一定的借鉴作用。     

Conditional VaR using EVT - Towards a planned margin scheme

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series — dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.