基于KMV模型的我国行业信用风险实证研究

发布时间：2018-05-15 12:41

本文选题：KMV模型 + 极端违约距离　；参考：《厦门大学》2014年硕士论文

【摘要】：由美国次贷危机引发的全球金融危机,令国际金融界开始意识到随着金融领域的不断变革、金融衍生工具的不断推出,信用风险已经成为金融业最主要的风险之一。本文使用KMV模型计算违约距离(Default-Distance,DD)和极端违约距离(Extreme-Default-Distance,Ex_DD)测度正常环境与极端环境下的信用风险,用于我国行业间信用风险的对比分析。通过归纳国内对KMV模型参数估计修正的研究成果,选择了GARCH(1,1)模型估计KMV模型的核心参数股权价值波动率,从而提高模型的估计精度。之后,按照申银万国一级行业分类标准选取了房地产、汽车、有色金属、钢铁、电子、金融、农林牧渔、能源8个行业沪深两市A股上市的272家公司2004-2009年的数据进行了实证分析。实证结果表明,DD与Ex_DD描绘的行业信用风险走势时能够较好地反映我国宏观经济走势,并对宏观经济环境的恶化有预警作用；在极端经济环境下DD值可能低估行业信用风险,使用Ex DD保守估计行业信用风险时,可放大行业信用风险波动,对区分极端经济环境下不同行业违约风险差异有较好作用；有色金属、电子、房地产在DD测度下属于高风险行业,其中有色金属及电子行业受其产业结构特征影响,高风险主要表现为较高的行业波动；钢铁行业与农林牧渔均属于DD测度下的低风险行业,但背后支持的原因却不尽相同,钢铁行业虽信用风险排名很好,但掩盖在政策支持下面的产能过剩、结构转型问题却成为较大的风险隐患；在极端经济环境下,钢铁行业信用风险指标波动剧烈,风险有所暴露,汽车行业危机期间信用风险迅速攀升,表现出强劲的周期性调整,电子行业则表现出会比整个宏观经济回暖更快的行业特质。基于本文结论,笔者提出了该模型在银行等金融机构风险控制与创造利润方面的应用展望。
[Abstract]:The global financial crisis caused by the subprime mortgage crisis in the United States has made the international financial circles begin to realize that with the continuous changes in the financial field and the introduction of financial derivatives, credit risk has become one of the most important risks in the financial industry. In this paper, the KMV model is used to calculate the default distance (Default-Distance DDD) and the extreme default distance (Extreme-Default-Distance) to measure the credit risk in normal and extreme environments. By summing up the domestic research results of parameter estimation correction of KMV model, this paper selects the Garch 1) model to estimate the volatility of equity value of the core parameter of KMV model, so as to improve the estimation accuracy of the model. After that, according to the first level industry classification criteria of Shenyin Wanguo, real estate, automobiles, non-ferrous metals, iron and steel, electronics, finance, agriculture, forestry, herding and fishing were selected. The data of 272 companies listed in Shanghai and Shenzhen A-shares in 8 energy industries from 2004 to 2009 are analyzed empirically. The empirical results show that the trend of industry credit risk described by DD-D and Ex_DD can better reflect the macroeconomic trend of our country, and it can warn the deterioration of the macroeconomic environment, and the DD value may underestimate the credit risk of the industry in extreme economic environment. Using Ex DD to estimate industry credit risk conservatively, it can amplify the fluctuation of industry credit risk and has a better effect in distinguishing the difference of default risk between different industries in extreme economic environment; Non-ferrous metals, electronics, non-ferrous metals, electronic, non-ferrous metal, electronic, Real estate belongs to high risk industry under DD measure, in which non-ferrous metal and electronic industry is affected by its industrial structure characteristic, the high risk mainly shows as high industry fluctuation; Iron and steel industry, agriculture, forestry, animal husbandry and fishery belong to low risk industry under DD measure. However, the reasons behind the support are not the same. Although the steel industry has a good credit risk ranking, but the overcapacity hidden under the policy support, the structural transformation problem has become a major risk; in the extreme economic environment, The credit risk index of iron and steel industry fluctuates sharply and the risk is exposed. During the crisis of automobile industry, the credit risk rises rapidly, showing strong cyclical adjustment, and the industry characteristic of electronics industry shows a faster recovery than the whole macro economy. Based on the conclusion of this paper, the author puts forward the application prospect of this model in risk control and profit creation of financial institutions such as banks.
【学位授予单位】：厦门大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F832.4

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