The Study Of Distance-To-Default Based On DEA

With the increased integration of global economy, China continues to open up the financial industry. Finance and related fields become the wind vane of economic situation, and credit risk once again come to the focus of the people all over the world. Compare with foreign banks and professional credit rating company, our financial and credit rating industry have a disadvantage on tools which are used to analyze credit risk. Meanwhile, the methods for credit risk analysis vary a lot, and models for internal rating uses are simple. Consider that in China there is only a short period for banks to put the models into practice, thus often more complex models have to be purchased from foreign companies, which can not well adapt to the conditions of our country. The level of risk control has become the weak links for China's financial industry in the international competition. Therefore, to learn and study the advanced management methods and technology is of great importance for the development of our quantitative credit risk methods.KMV model, a measurement tool of credit risk which is developed by KMV Risk Management Company, was promoted as an Internal Ratings-Based Approach for banks by the Basel Committee on Banking Supervision in the "New Basel Capital Accord" in 2004, which demonstrating its widely recognized in foreign countries. However, the model is only a conceptual framework, there is no specific formula, and the most critical part of the model is the option pricing formula using the VK (Vasicek-Kealhofer) model. The relationship between equity value of assets and volatility function in VK model is the trade secrets of KMV Company, thus was not published. Since the Model was built in a different economic environment from our country, and the model itself has limitations, we need to test and improve model before putting it into practices in our country. Default Distance (DD) in the model is an indicator used to measure the default risk. It shows the required minimum capital gains from the current state to default, defined as the multiples of the distance between the expected future value and the Default Point to the future capital gains in the standard deviation. Default distance is a typical early-warning indicator for assessing the credit risk and is of great value. As an intermediate variable in the KMV model, Default Distance is calculated in the framework of KMV model in all kinds of literature. The main work of this paper is to calculate the Default distance using CCR model in DEA methodology to quantify the credit risk, after studying the existing KMV Default distance model. The principle idea when applying DEA method to the calculation of default distance is to use DEA value of the company to replace the market value of the company in KMV model, and use average DEA points of ST companies within the industry instead of Default Point, then get the Default Distance.Data envelopment analysis (DEA) methods as a method of operations research have been widely applied to various fields in recent years. The main advantage of DEA is that it is a non-parametric method which does not require pre-estimated parameters, does not require an assumptive function form for the input-output indicators. This character can avoid subjective factors and reduce errors when the relationships between the indicators are unknown, also can calculate in some of the hidden relationships. DEA Method can be used as long as inputs and outputs indicators in line with the model, which expanding the scope of the data can be adopted and the applicability of the model in practical applications. At the end of 1990, DEA was first used in the credit scoring and banking performance, and gradually become a popular tool for financial analysis.This article is divided into four chapters, and the main ideas of chapters are as follows:The Chapter I is the Introduction. This part briefly introduced the background of the paper and the main credit risk measurement models.Chapter II is a theoretical basis. In this chapter Default Distance in KMV model was introduced, as well as the difficulties of using KMV model in China. Then the main tool for this paper was introduced, that is Data Envelopment Analysis (DEA) method. Chapter III of this paper is to introduce the specific settings of model in this paper, and describe the principles of the sample selection as well as the financial ratios selection.Chapter IV is an empirical analysis which using CCR model in DEA to analysis data of Chinese medicine and biological companies in 2006 and 2007, verify that the DEA method can effectively determine a maybe ST company.Finally, according to the empirical results of the analysis and the previous results of literature, I arrive at the conclusion that DEA method has practical value and is worth to explore deeply in the field of credit risk management. Some shortcomings and the possible developed direction of this model are analyzed, and then suggestions are given to the bio-medical industry.