【作者】 吴敏；

【导师】 曾祥金；

【作者基本信息】 武汉理工大学 ， 应用数学， 2009， 硕士

【摘要】 自我国加入WTO,全球金融一体化加速了我国利率市场化的进程,市场利率不再由中央银行完全主导,而是逐步由市场供求机制所决定。利率的频繁变动给银行财务状况带来了潜在的风险,从而加大了商业银行亏损或倒闭的可能性。并且越来越多的研究也表明我国商业银行存在较大的利率风险,这使得利率风险管理问题成为人们关注的焦点。为了有效的进行商业银行利率风险管理即控制和防范利率风险,首要的任务是要能够很好的度量利率风险。如何采用科学的方法度量利率风险则成为我国商业银行的核心问题。本文则是主要讨论VaR和CVaR这两种风险度量方法在我国商业银行利率风险度量中的应用。文章重点分为四个部分:第一部分回顾了已有的商业银行利率风险度量方法并比较其优缺点;第二部分着重讨论了VaR模型的基本概念,主要的计算方法以及VaR模型在应用中的局限性;第三部分介绍CVaR模型的定义,性质、基本的计算以及相对于VaR模型的优势;第四部分则是选取2007年1月4日至2007年9月6同我国商业银行问同业拆借隔夜利率、7日利率以及中信标普国债指数收盘数据进行实证分析,结合已有的运用VaR方法度量商业银行利率风险的研究成果,将基于GARCH计算的CVaR风险度量方法引入到商业银行利率风险度量中,建立基于AR-GARCH的VaR和CVaR模型。根掘所得到的VaR和CVaR估计值定量的模拟考察我国商业银行面临的利率风险状况。研究结果表明,AR(1)-GARCH(1,2)、AR(1)-GARCH(1,1)能够较好的模拟我国商业银行间同业拆借利率收益的波动,AR(2)-GARCH(1,1)对中信标普国债指数收益率波动的模拟也比较好,可见基于AR-GARCH的VaR和CVaR模型在度量商业银行利率风险时都有较好的适用性。并且由实证分析得到CVaR的估计值要大于VaR的估计值,而且要高出许多,说明CVaR能够度量覆盖更大范围的左尾风险。这是由于CVaR方法满足次可加性的这条性质优于VaR方法,和基于GARCH的VaR方法相比,能更好的对尾部风险进行控制。但因为我国银行间同业拆借市场和银行间债券市场并不完善,基于所选用的样本数量较小以及所使用的方法不够全面,本文所采用的模型并不能说明所有的问题,因此在文章结尾处提出了本文需进一步研究的方向。更多还原

【Abstract】 Since China’s accession to WTO, global financial integration has accelerated the process of China’s liberalization of interest rates. Market interest rates have no longer completely dominated by the central bank, but gradually determined by market supply and demand mechanism. Frequent changes of interest rates brought potential risks to the financial situation of the banks, it has increased the possibility of losses or failures of the commercial banks. And a large number of research also show that the interest rate risk of Chinese commercial banks is more and more obvious, which makes people pay great attention to the interest rate risk management. In order to effectively supervise interest rate risk of commercial banks that control and prevent interest rate risk, first and foremost task is to have a good method to measure interest rate risk. How to apply scientific methods to measure interest rate risk has become the core issue of Chinese commercial banks. This article is mainly to discuss the application of VaR and CVaR these two risk measurement methods in the Chinese commercial banks interest rate risk measurement.The paper is divided into four parts: The first section reviews the existing interest rate risk measurement methods and compare their advantages and disadvantages; The second part focuses on the basic concepts of the VaR model, the main method of calculation and the limitations of the application; The third section describes the CVaR model’s definition, nature, basic computing and the advantages relative to VaR model; The fourth part selects some data such as Chinese commercial overnight inter-bank lending rate, the interest rate on the 7th and the CITIC S&P government bond index data from January 4th, 2007 to September 6th, 2007 to have empirical analysis. Combining the research results of the existing interest rate risk measurement methods of commercial banks, we introduced the model CVaR based on AR-GARCH into commercial banks to measure interest rate risk, built the model VaR and CVaR based on AR-GARCH. According to the estimates of VaR and CVaR, we may obtain quantitative simulation study of Chinese commercial banks facing interest rate risk profile.The results show that , the model AR(1)-GARCH(1,2), AR(1)-GARCH(1,1) can be better to simulate the fluctuations in earnings of Chinese commercial inter-bank lending interest rate, AR(2)-GARCH( 1,1) that simulates the CITIC S&P government bond index data is also relatively good, We can see that the model VaR and CVaR calculated based on AR-GARCH can be well applied in the measurement of commercial bank interest rates. The estimate of CVaR obtained from the empirical analysis is bigger than the estimated value VaR, and to much higher, which illustrates CVaR can measure a wider coverage of the left tail risk than VaR. This is because CVaR possess the characteristic as subadditivity while VaR has no this nature, so compared to VaR calculated based on AR-GARCH, CVaR can be better to control the risks of the tail. Because of China’s inter-bank lending market and inter-bank bond market is not perfect, the number of samples selected is relatively smaller and the method used is not comprehensive enough, the model used in this paper does not explain all the problems, therefore, this paper provides a set of suggestions for further research in the end.更多还原