商业银行信用风险度量与管理研究
摘要
如何对信用风险进行度量和管理是商业银行经营永恒的主题。近年来商业银行面临的信用风险越来越大,越来越复杂,备受银行体系以及经济主体乃至监管当局所关注。本文以商业银行为视角,研究其风险管理最重要的一环,即对贷款企业信用风险的度量与管理,以期对发展中的我国商业银行提供技术和方法。
本文首先界定信用风险概念和特点,对风险管理领域的理论背景、信用风险度量方法以及国内外的研究状况进行全面的归纳和整理,为本文的研究提出思路和方向。研究以风险度量方法的改进和实证分析为重点,运用上市公司所披露的财务信息,建立了上市公司信用风险评价指标体系,提出信用风险度量的模糊神经网络方法。通过与上海某商业银行的合作,对其1999-2005年的贷款明细和公司财务数据进行了系统研究,运用粗糙集理论的约简功能,从中选出最能反映企业信用状况的8项财务指标,再应用神经网络方法进行信用评价。实证研究表明所提方法具有较高精度。
本文利用CCER提供的上市公司个股行情数据和财务数据,进行了KMV方法的实证研究,对其违约距离计算公式进行了对比和改进,得出了适合中国国情的具体操作方法。对于非上市公司信用风险的动态度量问题,本文研究了KMV模型发展而来的PFM模型,并结合我国实际情况进行改进,采用神经网络估计方法估计非上市公司的资产价值和波动率,用资产保值增值率代替资产的连续回报,进行违约距离计算。实证研究表明,本文所提方法对我国上市公司、非上市公司具有较好的信用风险评价和预测能力。
本文论述了风险管理组织机构的优化,信息系统的完善、风险管理文化和理念的培育对信用风险量化管理的重要意义,提出银行进行这些方面改革的具体思路。
最后,总结了全文的主要研究成果,并对今后的研究方向进行了分析。
How to measure and manage credit risk is an eternal subject concerning the commercial bank management. In recent years, the credit risk faced by commercial banks has been becoming bigger and more complex, which results in more attention from the bank system, the economic subjects and even the supervising and managing authority. This paper examines the most important part of risk management,that’s, the risk measurement and management of the enterprises with loans, from the aspect of commercial banks, with a desire to provide the technology and methods for our commercial banks in development.
The concept of credit risk and its characteristics are defined firstly. To express our research structure and direction we make a comprehensive induction and reorganization of the risk management background theories, the measurement methods of credit risk, as well as the domestic and foreign findings. To focus on the improvement of the risk measurement assessment system and empirical research, the index assessment system of the credit risk is established based on financial information disclosed by listed companies, and the fuzzy neural network method to measure the credit risk is proposed. In cooperation with a commercial bank in Shanghai, studying their loan details and their corporate finance data between 1999 and 2005, 8 financial indexes which can reflect the enterprise’s credit condition most are selected by utilizing rough sets, and the credit appraisal is carried on with the neural network method again. Empirical research shows that the proposed method appears quite precise.
With the listed companies’stock quotation data and the financial data provided by CCER, the empirical research of KMV method is conducted to contrast and improve its calculating formulas for violation distance, and operating procedure that suits to the Chinese national condition is obtained. To measure dynamic credit risk of non-listed companies, we study the PFM model developed from the KMV model, where the neural network is used to estimate non-listed companies‘assets value and undulation rate and the value-added rate is used to replace continuous rate of returns to carry on the violation distance calculations. The result shows that the method has good credit risk assessment and forecast ability for both listed and non-listed companies.
We also discuss the optimization of the organization structures of credit risk management, the improvement of the information system, and the significance of credit risk management culture and idea to the quantitative management. Then concrete reform paths for banks are suggested.
Finally, we also summarize our main results and point out its future research direction.
本文首先界定信用风险概念和特点,对风险管理领域的理论背景、信用风险度量方法以及国内外的研究状况进行全面的归纳和整理,为本文的研究提出思路和方向。研究以风险度量方法的改进和实证分析为重点,运用上市公司所披露的财务信息,建立了上市公司信用风险评价指标体系,提出信用风险度量的模糊神经网络方法。通过与上海某商业银行的合作,对其1999-2005年的贷款明细和公司财务数据进行了系统研究,运用粗糙集理论的约简功能,从中选出最能反映企业信用状况的8项财务指标,再应用神经网络方法进行信用评价。实证研究表明所提方法具有较高精度。
本文利用CCER提供的上市公司个股行情数据和财务数据,进行了KMV方法的实证研究,对其违约距离计算公式进行了对比和改进,得出了适合中国国情的具体操作方法。对于非上市公司信用风险的动态度量问题,本文研究了KMV模型发展而来的PFM模型,并结合我国实际情况进行改进,采用神经网络估计方法估计非上市公司的资产价值和波动率,用资产保值增值率代替资产的连续回报,进行违约距离计算。实证研究表明,本文所提方法对我国上市公司、非上市公司具有较好的信用风险评价和预测能力。
本文论述了风险管理组织机构的优化,信息系统的完善、风险管理文化和理念的培育对信用风险量化管理的重要意义,提出银行进行这些方面改革的具体思路。
最后,总结了全文的主要研究成果,并对今后的研究方向进行了分析。
How to measure and manage credit risk is an eternal subject concerning the commercial bank management. In recent years, the credit risk faced by commercial banks has been becoming bigger and more complex, which results in more attention from the bank system, the economic subjects and even the supervising and managing authority. This paper examines the most important part of risk management,that’s, the risk measurement and management of the enterprises with loans, from the aspect of commercial banks, with a desire to provide the technology and methods for our commercial banks in development.
The concept of credit risk and its characteristics are defined firstly. To express our research structure and direction we make a comprehensive induction and reorganization of the risk management background theories, the measurement methods of credit risk, as well as the domestic and foreign findings. To focus on the improvement of the risk measurement assessment system and empirical research, the index assessment system of the credit risk is established based on financial information disclosed by listed companies, and the fuzzy neural network method to measure the credit risk is proposed. In cooperation with a commercial bank in Shanghai, studying their loan details and their corporate finance data between 1999 and 2005, 8 financial indexes which can reflect the enterprise’s credit condition most are selected by utilizing rough sets, and the credit appraisal is carried on with the neural network method again. Empirical research shows that the proposed method appears quite precise.
With the listed companies’stock quotation data and the financial data provided by CCER, the empirical research of KMV method is conducted to contrast and improve its calculating formulas for violation distance, and operating procedure that suits to the Chinese national condition is obtained. To measure dynamic credit risk of non-listed companies, we study the PFM model developed from the KMV model, where the neural network is used to estimate non-listed companies‘assets value and undulation rate and the value-added rate is used to replace continuous rate of returns to carry on the violation distance calculations. The result shows that the method has good credit risk assessment and forecast ability for both listed and non-listed companies.
We also discuss the optimization of the organization structures of credit risk management, the improvement of the information system, and the significance of credit risk management culture and idea to the quantitative management. Then concrete reform paths for banks are suggested.
Finally, we also summarize our main results and point out its future research direction.
引文
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