基于證券交易信息的債券市場信用風(fēng)險研究
發(fā)布時間：2018-11-15 08:16
【摘要】：近年來中國債券市場經(jīng)歷了快速的發(fā)展,但是信用風(fēng)險監(jiān)管的步伐卻沒有跟上。從2014年起國內(nèi)信用債違約事件開始增加,2016年國內(nèi)全年違約債79支,違約金額高達(dá)403億元。從國內(nèi)債券市場信用風(fēng)險監(jiān)管及預(yù)警機制來看,評級不客觀,跟蹤不及時以及評級方法落后的現(xiàn)象普遍存在。因此充分結(jié)合大數(shù)據(jù)等科技的發(fā)展,研究適合國內(nèi)債券市場的信用風(fēng)險監(jiān)管方法迫在眉睫。在對國內(nèi)債券市場的發(fā)展?fàn)顩r進(jìn)行梳理和國內(nèi)信用評級現(xiàn)狀進(jìn)行分析的基礎(chǔ)上,本文結(jié)合國內(nèi)外理論研究成果、國內(nèi)市場可用信息、大數(shù)據(jù)挖掘及自動化技術(shù),采用證券交易信息,構(gòu)建信用風(fēng)險度量和預(yù)警模型�；趥灰仔畔⒑蛡A(chǔ)屬性,構(gòu)建DS模型。根據(jù)計算所得風(fēng)險中性違約概率轉(zhuǎn)換的信用分,度量和預(yù)測債券信用風(fēng)險�；谏鲜泄景l(fā)行主體的財務(wù)和股票交易信息,結(jié)合BS期權(quán)定價理論,構(gòu)建KMV模型。根據(jù)計算所得違約距離,度量和預(yù)測上市公司發(fā)行主體的信用風(fēng)險。本文解決了由于信息不完全造成的信用評級不客觀甚至無評級信息的問題,打破了傳統(tǒng)信用評級中所需財務(wù)信息不連續(xù)和時間滯后的局限。債券市場專業(yè)信用評級機構(gòu)下調(diào)債券或者發(fā)行主體的信用級別后,會引起債券到期收益率的顯著提高。但是在債券信用級別下調(diào)之前,一些債券的到期收益率已明顯提高。部分投資者對債券信用風(fēng)險的察覺早于專業(yè)信用評級機構(gòu)�；趥灰仔畔�(gòu)建的DS模型在國內(nèi)債券市場是有效的。DS模型對于信用狀況惡化的債券,具有顯著的預(yù)警功能。DS模型對信用狀況趨好的債券的預(yù)測能力要弱于對信用狀況惡化債券的預(yù)測能力。但是國外評級機構(gòu)常用的經(jīng)典KMV模型卻在國內(nèi)市場失效。根據(jù)KMV計算所得的發(fā)債主體違約距離不服從正態(tài)分布,所以不可以直接轉(zhuǎn)換成違約概率。當(dāng)模型參數(shù)變化時,違約距離隨著發(fā)債主體信用狀況的分布情況會發(fā)生巨大的變化。整體來看,隨著發(fā)行主體信用狀況級別變化,違約距離期望值的變化具有一定的規(guī)律性。當(dāng)發(fā)行主體信用級別處于A層及其以上時,發(fā)債主體信用狀況越好,違約距離越小。但是當(dāng)發(fā)債主體信用狀況處于A層及其以下時,發(fā)債主體信用狀況越好,違約距離越大。
[Abstract]:China's bond market has experienced rapid development in recent years, but the pace of credit risk regulation has not kept pace. The number of defaults on domestic credit bonds has increased since 2014, with 79 defaults in 2016, totaling 40.3 billion yuan. Judging from the credit risk supervision and early warning mechanism of domestic bond market, the phenomenon of rating is not objective, tracking is not timely and the method of rating is backward. Therefore, with the development of big data and other science and technology, it is urgent to study the credit risk supervision method suitable for domestic bond market. On the basis of combing the development of domestic bond market and analyzing the present situation of domestic credit rating, this paper combines the theoretical research results at home and abroad, the information available in the domestic market, big data mining and automation technology. The model of credit risk measurement and early warning is constructed by using securities trading information. Based on bond trading information and bond basic properties, the DS model is constructed. The credit risk of bond is measured and forecasted according to the credit score of risk neutral probability conversion. Based on the financial and stock trading information of the issuers of listed companies, the KMV model is constructed based on the BS option pricing theory. According to the calculated default distance, the credit risk of the issuer of listed companies is measured and forecasted. This paper solves the problem that credit rating is not objective or even without rating information caused by incomplete information, and breaks the limitation of discontinuity of financial information and time lag in traditional credit rating. The bond market professional credit rating agencies reduce the credit rating of the bond or issuer, which will lead to a significant increase in bond maturity yield. But before the downgrade, some bonds had significantly higher yields on maturities. Some investors perceived bond credit risk earlier than professional credit rating agencies. The DS model based on bond trading information is effective in the domestic bond market. The prediction ability of DS model for bonds with better credit status is weaker than that for bonds with deteriorating credit status. However, the classical KMV model commonly used by foreign rating agencies fails in the domestic market. The default distance of the issuer calculated by KMV is not from normal distribution, so it can not be directly converted into default probability. When the model parameters change, the distance of default will change greatly with the distribution of the credit status of the issuer. As a whole, with the credit status of the issuer changing, the variation of the distance from default to expectation has certain regularity. When the credit grade of the issuer is above the A level, the better the credit condition of the issuer is, the smaller the default distance is. However, when the credit status of the issuer is in the A level and below, the better the credit condition of the issuer is, the greater the distance of default is.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51
本文編號：2332712
[Abstract]:China's bond market has experienced rapid development in recent years, but the pace of credit risk regulation has not kept pace. The number of defaults on domestic credit bonds has increased since 2014, with 79 defaults in 2016, totaling 40.3 billion yuan. Judging from the credit risk supervision and early warning mechanism of domestic bond market, the phenomenon of rating is not objective, tracking is not timely and the method of rating is backward. Therefore, with the development of big data and other science and technology, it is urgent to study the credit risk supervision method suitable for domestic bond market. On the basis of combing the development of domestic bond market and analyzing the present situation of domestic credit rating, this paper combines the theoretical research results at home and abroad, the information available in the domestic market, big data mining and automation technology. The model of credit risk measurement and early warning is constructed by using securities trading information. Based on bond trading information and bond basic properties, the DS model is constructed. The credit risk of bond is measured and forecasted according to the credit score of risk neutral probability conversion. Based on the financial and stock trading information of the issuers of listed companies, the KMV model is constructed based on the BS option pricing theory. According to the calculated default distance, the credit risk of the issuer of listed companies is measured and forecasted. This paper solves the problem that credit rating is not objective or even without rating information caused by incomplete information, and breaks the limitation of discontinuity of financial information and time lag in traditional credit rating. The bond market professional credit rating agencies reduce the credit rating of the bond or issuer, which will lead to a significant increase in bond maturity yield. But before the downgrade, some bonds had significantly higher yields on maturities. Some investors perceived bond credit risk earlier than professional credit rating agencies. The DS model based on bond trading information is effective in the domestic bond market. The prediction ability of DS model for bonds with better credit status is weaker than that for bonds with deteriorating credit status. However, the classical KMV model commonly used by foreign rating agencies fails in the domestic market. The default distance of the issuer calculated by KMV is not from normal distribution, so it can not be directly converted into default probability. When the model parameters change, the distance of default will change greatly with the distribution of the credit status of the issuer. As a whole, with the credit status of the issuer changing, the variation of the distance from default to expectation has certain regularity. When the credit grade of the issuer is above the A level, the better the credit condition of the issuer is, the smaller the default distance is. However, when the credit status of the issuer is in the A level and below, the better the credit condition of the issuer is, the greater the distance of default is.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F832.51
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 賀思輝;李正賓;;基于轉(zhuǎn)移概率矩陣的企業(yè)債券定價模型[J];統(tǒng)計與信息論壇;2017年01期
2 曹勇;李孟剛;李剛;洪雅惠;;基于信用利差與Logistic回歸的公司違約概率測算模型與實證研究[J];運籌與管理;2016年06期
3 韋茜;李立平;董哲;;基于KMV模型的公司債券信用風(fēng)險研究[J];財會通訊;2016年35期
4 高見;尹小兵;;風(fēng)險平價策略及其在投資管理中的運用[J];證券市場導(dǎo)報;2016年12期
5 李祥琪;趙紅巖;;中小企業(yè)集合債券信用風(fēng)險研究——基于KMV模型[J];時代金融;2016年33期
6 楊寶臣;馬志茹;蘇云鵬;;中國公司債券的信用利差與流動性風(fēng)險[J];技術(shù)經(jīng)濟(jì);2016年11期
7 楊寶臣;張涵;;中國債券市場時變風(fēng)險溢價——遠(yuǎn)期利率潛在信息[J];管理科學(xué);2016年06期
8 徐亞娟;王過京;;約化模型中含有交易對手信用風(fēng)險的可轉(zhuǎn)換債券的定價[J];應(yīng)用概率統(tǒng)計;2016年05期
9 韓國文;黃笑言;趙剛;;中美德國債收益率曲線的共同影響因素[J];金融論壇;2016年10期
10 張志軍;陳詣輝;陳秉正;;債券市場的信用風(fēng)險防范[J];中國金融;2016年19期
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