本文關(guān)鍵詞：發(fā)行公司債券的上市公司信用風(fēng)險(xiǎn)度量研究　出處：《上海交通大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 上市公司 公司債券 KMV模型 信用風(fēng)險(xiǎn) 違約距離

【摘要】：近年來(lái)，中國(guó)公司債券發(fā)行規(guī)模逐年遞增，這就意味著其信用風(fēng)險(xiǎn)的識(shí)別和控制將會(huì)是金融市場(chǎng)面臨的重要問(wèn)題。而我國(guó)信用風(fēng)險(xiǎn)度量的方法和理念相對(duì)于國(guó)際發(fā)達(dá)國(guó)家的水平仍然存在很大的差距�，F(xiàn)今信用評(píng)級(jí)結(jié)果一般來(lái)講是衡量信用風(fēng)險(xiǎn)的重要工具，在中國(guó)卻沒(méi)有發(fā)揮其真正的意義，被社會(huì)公眾過(guò)度依賴。KMV模型，作為一種在國(guó)外成熟市場(chǎng)備受肯定的結(jié)構(gòu)化信用風(fēng)險(xiǎn)度量和預(yù)測(cè)工具，能夠提供動(dòng)態(tài)和及時(shí)的信用風(fēng)險(xiǎn)監(jiān)控。 為了適合中國(guó)發(fā)行公司債券的上市公司信用風(fēng)險(xiǎn)度量，本文對(duì)KMV模型進(jìn)行參數(shù)上的修正。對(duì)修正后的KMV模型，通過(guò)MATLAB軟件算出制造業(yè)中發(fā)行公司債券的債券主體信用級(jí)別“高”和“低”兩組的違約距離，結(jié)果信用級(jí)別“高”的一組違約距離均值顯著大于“低”的一組，表明該KMV模型能較好地區(qū)分發(fā)行公司債券的上市公司的信用風(fēng)險(xiǎn)。在證明KMV模型適用性之后，本文通過(guò)比較單純考慮信用評(píng)級(jí)對(duì)債券收益率的解釋力和將信用評(píng)級(jí)與違約距離相結(jié)合對(duì)債券收益率的解釋力，得出傳統(tǒng)信用評(píng)級(jí)結(jié)果和修正的違約距離相結(jié)合的效果更好，從而肯定了KMV模型的應(yīng)用意義。文章同時(shí)對(duì)債券主體按行業(yè)分析，可得房地產(chǎn)行業(yè)違約距離均值相對(duì)低，信用風(fēng)險(xiǎn)較高。最后對(duì)違約距離進(jìn)行敏感性分析，可得股權(quán)價(jià)值波動(dòng)率對(duì)違約距離最敏感。 中國(guó)評(píng)級(jí)機(jī)構(gòu)對(duì)公司債券和債券主體的評(píng)級(jí)成本高，歷時(shí)長(zhǎng)，不能及時(shí)準(zhǔn)確地反映公司債券相應(yīng)的信用風(fēng)險(xiǎn)情況。所以本文提出修正的KMV模型能達(dá)到較好度量發(fā)行公司債券的上市公司信用風(fēng)險(xiǎn)的效果，有助于改進(jìn)信用評(píng)級(jí)技術(shù)方法，，完善中國(guó)信用評(píng)級(jí)體系，從而促進(jìn)中國(guó)資本市場(chǎng)更加科學(xué)、健康的發(fā)展，同時(shí)利用修正KMV模型的結(jié)果也可為投資者提供參考。
[Abstract]:In recent years, the issuance scale of Chinese corporate bonds has been increasing year by year. This means that the identification and control of credit risk will be an important problem facing the financial market, and there is still a big gap between the methods and concepts of credit risk measurement in China compared with the level of developed countries. Generally speaking, credit rating results are an important tool to measure credit risk. However, it has not played its true significance in China, and has been over-dependent by the public. KMV model is regarded as a well-established structured credit risk measurement and forecasting tool in foreign mature markets. Ability to provide dynamic and timely credit risk monitoring. In order to measure the credit risk of listed companies which issue corporate bonds in China, this paper modifies the parameters of the KMV model and the modified KMV model. Through the MATLAB software, the default distance of the main credit level of the corporate bond issuers in the manufacturing industry is calculated in the two groups of "high" and "low". As a result, the average default distance between the "high" group and the "low" group was significantly higher than that of the "low" group. The results show that the KMV model can better distinguish the credit risk of listed companies issuing corporate bonds. After proving the applicability of the KMV model. This paper compares the explanatory power of credit rating on bond yield and the combination of credit rating and default distance on bond yield. It is concluded that the combination of the traditional credit rating results and the modified default distance is better, and the application significance of the KMV model is confirmed. At the same time, the paper analyzes the bond subject according to the industry. Finally, the sensitivity analysis of default distance is carried out, and the volatility of equity value is the most sensitive to default distance. Chinese rating agencies have a high cost and a long history of rating corporate bonds and bond subjects. The credit risk of corporate bonds can not be accurately reflected in time. Therefore, this paper proposes a modified KMV model to measure the credit risk of listed companies issuing corporate bonds. It is helpful to improve the technical method of credit rating, perfect the credit rating system of China, and promote the development of Chinese capital market more scientifically and healthily. At the same time, the modified KMV model can also be used as a reference for investors.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F275;F832.51;F224

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