中国金融不良贷款损失管理研究

发布时间：2018-05-07 08:34

本文选题：金融不良贷款 + 巴塞尔新资本协议　；参考：《北京交通大学》2012年博士论文

【摘要】：金融体系中,不良贷款的产生一直对金融机构构成着严重威胁,甚至是一个国家的经济。设立资产管理公司处置不良贷款是世界各国一项通行且行之有效的做法。源于我国银行金融机构经营模式,如何防范金融不良贷款的发生及发生后的损失管理问题研究成为是我国金融体系最核心的研究内容之一。特别是在我国开展实施巴塞尔Ⅱ新资本协议的今天,深入研究不良贷款的处置和损失问题对我国银行信用风险管理水平的提升及开发银行内部评级系统中的核心参数—违约损失率LGD都具有重大的现实意义。由于我国特殊的金融发展历程,我国银行业的违约不良贷款在2006年前基本划拨或出售给了四大资产管理公司。在新的金融环境下,根据国务院及财政部对资产管理公司新的发展定位,我国四大资产管理公司将不会结业清算,回归母体行,而是仍将以不良贷款处置为主业发展金融控股集团。因此,在未来可预见的时间内,资产管理公司仍将是我国金融不良贷款处置管理的主要力量。一方面,由于历史轨迹的改变,我国金融资产管理公司亟需通过对历史数据的采集、整理和挖掘,探索一条提升不良贷款处置管理水平的新途径；另一方面,由于我国金融不良贷款对银行金融机构的重大影响,研究不良贷款的特征及回收率估计模式,对我国新资本协议的实施不可或缺。违约损失数据库的建设在我国一直是个空白,利用大规模历史数据及其挖掘成果进行不良贷款处置管理的国内外研究和实证也很少。同时,与银行业、学术界和各信用评级机构所关注违约率的研究相比,由于数据缺乏、影响因素众多和形成原因复杂多样等问题,对违约损失率(LGD)国外学者也是从90年代中后期才开始关注,而国内的研究起步更晚,大部分还处于定性的描述,成熟的定量研究及模型开发几乎还是空白。由于信用环境和经营模式的差异,符合中国国情违约损失数据库的开发、数据的挖掘利用、违约损失率模型的开发研究都是我国金融业和学界亟需填补和完善的研究空白。由于违约损失率和回收率的互补关系,本文将交互使用这两个概念。本文尝试从资产管理公司的视角出发,结合新资本协议对金融风险管理的要求,通过建立我国大型的金融不良贷款损失数据库,在海量的不良贷款回收数据的基础上,总结归纳了我国不良贷款的特征和适合我国国情的违约损失率模型开发方法,并形成系统软件进行了实证。开拓性地研究了一套利用数据、挖掘成果、IT技术来整体提升资产管理公司不良贷款处置和损失管理的方法。具体结构如下：第二章综述和总结了不良贷款发生及处置清收的国内外模式及处置方法；第三章对不良贷款处置核心定价问题开展了理论研究。从金融资产定价理论出发,探讨了信用风险定价问题和围绕信用资产违约损失率计量的各种方法。追根溯源实现了不良贷款违约损失定价问题从理论到实践的全面梳理和研究；第四章在全面设计我国金融不良贷款数据库和数据准备的基础上,本文分析了影响我国不良贷款回收率水平的主要因素：宏观因素、行业地区因素、债务人因素、债项因素等；第五章总结了适合我国国情的不良贷款定价模型框架和回收率估计模型并对主要模型变量的贡献度进行了分析；第六章以资产管理公司为例,给出了在实际研究成果基础上所构建的我国第一个基于计算机技术的、从数据管理到过程风险管理到处置定价管理的实际管理框架。其中包括数据采集和数据处理,不良贷款处置方式的选择、不良贷款的定价及风险监测。应用实践表明,本研究的成果,不仅大大提高了资产管理公司的工作效率,而且也提高了不良贷款的回收率。第七章总结了全文的研究成果,并结合我国国情给出相应的政策建议。本文的创新点在于： (1)通过中国的海量不良贷款违约损失数据,实证总结了影响我国不良贷款回收率的基本因素：宏观经济因素、行业地区因素、债务人因素和债项因素。影响因素的得出为中国不良贷款的定价模型建设奠定了理论基础； (2)总结了适合我国国情的不良贷款回收率的建模方法应根据我国不良贷款的违约损失率U型分布的特征,通过先判别后回归的方式来进行回收率的计量工作,同时为提高模型实用性,应采取单户模型和打包模型结合的方式。同时模型建设中应该使用分布模型来更好的认识我国不良贷款的分布特征和影响因素； (3)研究总结了我国金融不良贷款违约损失数据库基本设计要素,包括：业务变量的内容,业务逻辑结构,数据采集清洗的有效机制。提出了我国金融环境下建立违约损失数据共享机制的必要性和重要意义,为今后我国金融不良贷款定价、管理及风险研究工作奠定了良好的研究基础； (4)通过实证研究,提出只有通过基础违约损失数据库的建设,在进行大量的数据特征和损失率影响因素挖掘的前提下,构建适合中国国情的模型才能有效的实现金融不良贷款的处置管理。同时为我国金融不良贷款处置管理及风险控制在整体决策层面上提供了实际案例和参考模式。
[Abstract]:In the financial system, the production of non-performing loans has been a serious threat to the financial institutions and even the economy of a country. Setting up a Asset Management Co to dispose of bad loans is a common and effective practice in the world. It is based on the management mode of the banking institutions in China and how to prevent the occurrence and occurrence of the financial non-performing loans. The research on loss management has become one of the core research contents of China's financial system.
Especially in the implementation of the implementation of the new Basel II capital agreement in China, it is of great practical significance to study the problem of the disposal and loss of non-performing loans for the improvement of the bank credit risk management level and the core parameter of the internal rating system of the bank - the default loss rate LGD.
Because of our country's special financial development course, the non-performing loan of the banking industry of our country basically allocated or sold to four Asset Management Co before 2006. Under the new financial environment, according to the new development orientation of the Asset Management Co, the State Council and the Ministry of finance will not settle the liquidation and return to the parent of the four Asset Management Co. Therefore, in the foreseeable future, Asset Management Co will still be the main force for the management of our financial non-performing loans in the foreseeable future. On the one hand, because of the change of the historical track, the financial Asset Management Co of our country needs to collect and collate the historical data. And explore a new way to improve the management level of non-performing loans. On the other hand, the study of the characteristics of non-performing loans and the estimation model of the recovery rate due to the significant impact of our non-performing loans on bank financial institutions can not be or lack of the implementation of the new capital agreement in China.
The construction of the default loss database has been a blank in our country. There are few domestic and international research and empirical research on the management of non-performing loans with large-scale historical data and their mining results. At the same time, compared with the research of the banking, academic and credit rating agencies, there are many factors affecting the default rate due to the lack of data. There are many problems in the formation of complex reasons. The foreign scholars of the default loss rate (LGD) have begun to pay attention to the late 90s, while the domestic research started later, most of them are still in the qualitative description. The mature quantitative research and model development are almost still blank. Due to the difference of the credit environment and management mode, it conforms to the breach of China's national conditions. The development of the loss database, the data mining and utilization, the development and research of the default loss rate model are all the research gaps that the financial industry and the academic community need to fill and perfect. The two concepts will be used in this paper because of the complementary relationship between the loss rate of default and the recovery rate.
From the perspective of the Asset Management Co, this paper tries to combine the requirements of the new capital agreement to the management of financial risk, and through the establishment of a large database for the loss of financial non-performing loans in China, and on the basis of a large amount of data on the recovery of non-performing loans, sums up the characteristics of the non-performing loans in China and the loss rate model suitable for the national conditions of our country. The method of development and the formation of system software have been demonstrated. A set of methods to improve the disposal and loss management of non-performing loans of Asset Management Co by using data, mining results and IT technology is explored. The concrete structure is as follows:
The second chapter summarizes and summarizes the domestic and international modes and disposal methods of the occurrence and disposal of non-performing loans.
The third chapter carries out a theoretical study on the core pricing of non-performing loans. Starting from the theory of financial asset pricing, this paper discusses the pricing of credit risk and the various methods of measuring the rate of default loss around the credit assets.
The fourth chapter, based on the comprehensive design of the database and data preparation of our country's non-performing loans, analyzes the main factors that affect the recovery of non-performing loans in China, such as macro factors, industry regional factors, debtor factors and debt factors.
The fifth chapter summarizes the pricing model framework and the rate of return model of the non-performing loans suitable for China's national conditions, and analyzes the contribution of the main model variables.
The sixth chapter, taking the Asset Management Co as an example, gives the actual management framework of China's first computer technology based on the actual research results, from data management to process risk management to disposal pricing management, including data collection and data processing, the choice of non-performing loans, and the determination of non-performing loans. Price and risk monitoring. Application practice shows that the results of this study not only greatly improve the efficiency of the Asset Management Co, but also improve the recovery rate of non-performing loans.
The seventh chapter summarizes the research results and gives corresponding policy recommendations in light of China's national conditions.
The innovation of this article lies in:
(1) through the data of large amount of non-performing loans in China, this paper empirically summarizes the basic factors that affect the recovery rate of non-performing loans in China: macroeconomic factors, industry regional factors, debtor factors and debt factors.
(2) the modeling method for the recovery rate of non-performing loans suitable for the national conditions of our country should be based on the characteristics of the U distribution of the default loss rate of the non-performing loans in China. The recovery rate should be measured by the first discriminant and post regression method. At the same time, in order to improve the practicability of the model, the single household model and the packaging model should be combined. The distribution model should be used to better understand the distribution characteristics and influencing factors of non-performing loans in China.
(3) the study summarizes the basic design elements of the default loss database of our country's NPLs, including the content of the business variables, the business logic structure, the effective mechanism of the data collection and cleaning, and puts forward the necessity and significance of establishing the data sharing mechanism of default loss under the financial environment of our country, and the pricing of our country's financial non-performing loans in the future. Management and risk research has laid a good foundation for research.
(4) through the empirical study, it is proposed that only through the construction of the database for the loss of basic breach of contract, under the premise that a large number of data features and the factors affecting the loss rate are excavated, a model suitable for China's national conditions can be constructed to effectively realize the disposal and management of the financial non-performing loans. At the same time, the management and risk control of the non-performing loans of China's financial system and the risk control are also proposed. It provides practical cases and reference models at the overall decision-making level.

【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：F832.4;F224

【参考文献】