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.     

模型危机还是应用不当造成的危机?——对《高盛风险之海市蜃楼》一文批驳VaR模型之反驳

次贷风波所引起的金融危机不但使国际银行业一度岌岌可危,而且沉重打击了世界经济。作为市场风险管理的核心,Vag模型遭到各方诟病。通过分析《高盛风险之海市蜃楼》一文,本文归纳了对Vag模型的几点认识误区,包括忽略对外部数据准确性的考核与检验,在模型应用上未能对正常市场情况与极端市场情况区别对待等。本文进而论证Vag仍是当前风险管理的首选工具,提出对收益率概率密度分布的选择将是Vag模型进一步发展的核心与关键,强调数据分析,指出量化研究应作为主观判断的前提与基础等观点。     

商业银行市场风险的度量框架研究

本文着眼于新巴塞尔协议下商业银行的市场风险管理,分析了我国银行业最突出的市场风险因素,较为细致地研究了市场风险的模型。结合我国商业银行实践,分析了国际上对市场风险的监管要求和度量框架,为我国商业银行的市场风险管理提供了参考意见。     

基于Copula理论的商业银行的市场风险研究

在投资者看好银行股的背景下,结合t-EGARCH模型和极值理论,利用Copula方法对14家上市银行股票进行分析,并通过蒙特卡洛模拟计算单只股票以及投资组合的VaR.结果表明,此方法能很好地量化风险,有助于衡量市场风险.     

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。     

长寿风险对基本医疗保险统筹基金的冲击效应研究——基于VaR与CVaR模型的综合测算

借鉴金融风险管理中VaR和CVaR模型对尾部风险的测量思路,通过构建有限数据Lee-Carter死亡率预测模型,测算了人口的长寿风险及其对基本医疗保险统筹基金的冲击效应,结果表明:2015-2060年基本医疗保险参保人群将面临巨大的长寿风险,极端情况下长寿风险将给统筹基金收支结余超预期下降造成不容忽视的尾部损失;推迟退休年龄、提高生育率、调整个人账户和报销比例、提高职工缴费工资和控制住院费用增长均可以在一定程度上缓解长寿风险的冲击.建议明确政府在基本医疗保险长寿风险管理中的主导作用,构建医疗、养老和长期护理保险的三险联动保障机制.     

银行操作风险度量方法比较研究

操作风险已经成为银行风险管理的重点,新巴塞尔协议因此对操作风险提出了资本要求,并且越来越多的国际活跃银行致力于开发适合自身特点的操作风险计量方法.文中对新巴塞尔协议推荐的操作风险度量方法:基本法、标准法、内部度量法和高级的损失分布法、极值理论模型进行了应用比较分析,并在此基础上提出了基于VaR的银行整体风险管理框架.     

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。     

Intraday hedging with financial options: the case of electricity

As market intermediaries, electricity suppliers purchase electricity from the wholesale market or self-generate to deliver their customers. However, electricity suppliers are uncertain about how much electricity their residential customers will use at any hour of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, suppliers generally rely on storage to manage it. Singularly, electricity suppliers are exposed to joint volumetric and price risk on an hourly basis given the physical attributes of electricity. In the literature on electricity markets, few articles compare the efficiency of forward contracts, options and physical assets (i.e. power plants) within intraday hourly hedging portfolios, whereas electricity markets are precisely hourly markets. We analyse portfolios made of forwards, options and/or power plants for specific hourly clusters (9 am, 12 pm, 18 pm, 9 pm) based on electricity market data from 2013 to 2015 from the integrated German–Austrian spot market. Through a VaR model, we prove that intraday hedging with forwards is structurally inefficient compared to financial options and physical assets, no matter the cluster hour. Moreover, our results demonstrate the contribution of ‘out of the money’ options for all hours within volatile spot markets.