基于信息视角的房地产公司违约风险度量研究

发布时间：2018-06-01 20:20

本文选题：房地产公司 + 违约风险　；参考：《清华大学》2013年硕士论文

【摘要】：近年来我国房地产业不确定性日益增加，行业内部出现明显分化，部分房地产公司的信用水平不断恶化。与此同时，，房地产业的外部融资环境正在发生深刻变化，金融脱媒和利率市场化的叠加效应势必会对准确有效的公司违约风险度量提出更高要求。但是，现有房地产公司违约风险度量方法在信息利用方面仍然存在不足，为了提高风险度量的准确性和可靠性，本文从信息视角构建了一个度量违约风险的完整框架。论文系统梳理了房地产公司违约的理论基础，指出信息不对称引致的信息优势方的机会主义行为是产生违约风险的根本原因。基于这一认识，本文主要从三个方面完善违约风险度量方法：一是在数据结构上，重点比较相同信息类型条件下静态数据结构和动态数据结构对模型判别能力的影响差异；二是在模型方法上，侧重于考察基于股票市场信息的结构化模型能否提供宏观经济和会计信息以外有关公司违约的额外信息；三是在信息类型方面，考虑到我国宏观经济信息、会计信息及市场信息等“硬”信息可能存在的信息质量问题，本文进一步从“软”信息入手，通过设计一种实证方法，从银企关系角度间接度量并检验“软”信息对违约风险度量模型的改进作用。基于本文收集整理的中国房地产上市公司银行信贷违约历史数据，论文实证研究发现：①在相同的“硬”信息条件下，相比静态数据模型，动态数据模型能够捕捉更多的有效信息，提高违约风险度量的准确性；②使用结构化模型预测中国房地产公司违约是有效的，但是相比宏观经济和会计信息，样本期内股票市场信息并不能提供有关公司违约的额外信息；③“软”信息对于改进违约风险度量模型具有重要价值，不仅能够显著提高风险判别的准确性，而且有助于减小不同数据结构对模型估计的影响。论文的研究结果为不同信息在房地产公司违约风险度量中的实际作用提供了经验证据，对于改进和完善公司违约模型具有借鉴意义。
[Abstract]:In recent years, the uncertainty of China's real estate industry is increasing, and the credit level of some real estate companies is deteriorating. At the same time, the external financing environment of the real estate industry is undergoing profound changes. The superposition effect of financial disintermediation and interest rate marketization will inevitably put forward higher requirements for accurate and effective corporate default risk measurement. In order to improve the accuracy and reliability of risk measurement, this paper constructs a complete framework to measure default risk from the perspective of information. The paper systematically combs the theoretical basis of the default of real estate companies, and points out that the opportunistic behavior of the information advantage party caused by asymmetric information is the fundamental reason for the risk of default. Based on this understanding, this paper mainly from three aspects to improve the default risk measurement methods: first, in the data structure, focus on the same information types of static data structure and dynamic data structure on the model discriminant ability difference; The second is whether the structured model based on stock market information can provide additional information about corporate default in addition to macroeconomic and accounting information, and the third is considering the macroeconomic information of our country in terms of information types, the second is whether the structured model based on stock market information can provide any additional information about corporate default in addition to macroeconomic and accounting information. Accounting information and market information and other "hard" information may exist information quality problems, this paper further from the "soft" information, through the design of an empirical method, Indirectly measure and test the "soft" information to improve the default risk measurement model from the perspective of the relationship between banks and enterprises. Based on the historical data of bank credit default of listed Chinese real estate companies collected in this paper, the empirical study shows that under the same "hard" information condition, compared with the static data model, Dynamic data model can capture more effective information and improve the accuracy of default risk measurement. 2 it is effective to use structured model to predict Chinese real estate company default, but compared with macroeconomic and accounting information, dynamic data model is effective. The stock market information in the sample period can not provide additional information about the company default. The soft information has important value for improving the default risk measurement model, and not only can significantly improve the accuracy of risk discrimination. It also helps to reduce the influence of different data structures on model estimation. The results of this paper provide empirical evidence for the actual role of different information in the measurement of default risk of real estate companies, and have reference significance for improving and perfecting the default model of real estate companies.
【学位授予单位】：清华大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F299.233.4;F832.4

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