本文選題：KMV模型 + 極端違約距離��； 參考：《廈門大學(xué)》2014年碩士論文

【摘要】：由美國次貸危機(jī)引發(fā)的全球金融危機(jī),令國際金融界開始意識(shí)到隨著金融領(lǐng)域的不斷變革、金融衍生工具的不斷推出,信用風(fēng)險(xiǎn)已經(jīng)成為金融業(yè)最主要的風(fēng)險(xiǎn)之一。本文使用KMV模型計(jì)算違約距離(Default-Distance,DD)和極端違約距離(Extreme-Default-Distance,Ex_DD)測度正常環(huán)境與極端環(huán)境下的信用風(fēng)險(xiǎn),用于我國行業(yè)間信用風(fēng)險(xiǎn)的對(duì)比分析。通過歸納國內(nèi)對(duì)KMV模型參數(shù)估計(jì)修正的研究成果,選擇了GARCH(1,1)模型估計(jì)KMV模型的核心參數(shù)股權(quán)價(jià)值波動(dòng)率,從而提高模型的估計(jì)精度。之后,按照申銀萬國一級(jí)行業(yè)分類標(biāo)準(zhǔn)選取了房地產(chǎn)、汽車、有色金屬、鋼鐵、電子、金融、農(nóng)林牧漁、能源8個(gè)行業(yè)滬深兩市A股上市的272家公司2004-2009年的數(shù)據(jù)進(jìn)行了實(shí)證分析。實(shí)證結(jié)果表明,DD與Ex_DD描繪的行業(yè)信用風(fēng)險(xiǎn)走勢時(shí)能夠較好地反映我國宏觀經(jīng)濟(jì)走勢,并對(duì)宏觀經(jīng)濟(jì)環(huán)境的惡化有預(yù)警作用；在極端經(jīng)濟(jì)環(huán)境下DD值可能低估行業(yè)信用風(fēng)險(xiǎn),使用Ex DD保守估計(jì)行業(yè)信用風(fēng)險(xiǎn)時(shí),可放大行業(yè)信用風(fēng)險(xiǎn)波動(dòng),對(duì)區(qū)分極端經(jīng)濟(jì)環(huán)境下不同行業(yè)違約風(fēng)險(xiǎn)差異有較好作用；有色金屬、電子、房地產(chǎn)在DD測度下屬于高風(fēng)險(xiǎn)行業(yè),其中有色金屬及電子行業(yè)受其產(chǎn)業(yè)結(jié)構(gòu)特征影響,高風(fēng)險(xiǎn)主要表現(xiàn)為較高的行業(yè)波動(dòng)；鋼鐵行業(yè)與農(nóng)林牧漁均屬于DD測度下的低風(fēng)險(xiǎn)行業(yè),但背后支持的原因卻不盡相同,鋼鐵行業(yè)雖信用風(fēng)險(xiǎn)排名很好,但掩蓋在政策支持下面的產(chǎn)能過剩、結(jié)構(gòu)轉(zhuǎn)型問題卻成為較大的風(fēng)險(xiǎn)隱患；在極端經(jīng)濟(jì)環(huán)境下,鋼鐵行業(yè)信用風(fēng)險(xiǎn)指標(biāo)波動(dòng)劇烈,風(fēng)險(xiǎn)有所暴露,汽車行業(yè)危機(jī)期間信用風(fēng)險(xiǎn)迅速攀升,表現(xiàn)出強(qiáng)勁的周期性調(diào)整,電子行業(yè)則表現(xiàn)出會(huì)比整個(gè)宏觀經(jīng)濟(jì)回暖更快的行業(yè)特質(zhì)�；诒疚慕Y(jié)論,筆者提出了該模型在銀行等金融機(jī)構(gòu)風(fēng)險(xiǎn)控制與創(chuàng)造利潤方面的應(yīng)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.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.4

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