Financing Lease Company Based On KMV Model:an Empirical Study Of Credit Risk Measurement

Along with the economic globalization and financial innovation, credit risk has become the one of the most important risk faced by financial institutions, to form its risk is also becoming more and more complicated and diverse, its risk is becoming more and more big, can effective financing lease company credit risk measurement and management, not only relates to the financing lease company’s own risk control and supervision, and is related to the whole social economy stable operation. With the needs of the development of financial leasing market, credit risk measurement method has been generally can’t adapt to the changing needs, how to accurately identify and measure the credit risk of default has become inevitable demand, according to the characteristics of the credit risk of listed companies financing lease industry, to predict and measure the size of the credit risk, which has its far-reaching significance for financial leasing industry. With the deepening reform of non-tradable shares and the financing lease business also gradually go deep into the national economy, financial leasing company’s risk management consciousness also gradually becomes more and more intense, risk regulation has become particularly important, but the measure of financing leasing company:an empirical study of credit risk is not much.It urges us to the credit risk measurement and management of financial leasing company in-depth research and analysis, choosing a suitable measuring approach to the financing lease company credit default risk, to reduce the credit risk, so that the financing lease company get better and faster development. KMV model is a kind of option pricing approach was applied to the credit risk management, and to measure the financial leasing company credit risk measurement model; If the KMV model can better be used in China’s listed companies financing lease, is as much as possible will be able to predict and avoid the credit risk, in order to promote the healthy and stable development of economy in our country.This paper first introduces the KMV model to financial leasing market, mainly from both theoretical and empirical direction for the analysis of financing lease in our country the credit risk of default. Theory, mainly through the second and third chapter to analyze financial leasing companies and the particularity of the credit risk measurement model, detailed introduces the KMV model, mainly includes the basic ideas of the model, assumptions and theoretical basis and model involved in the formula derivation process; Empirical aspect, concentrated in the fourth chapter, mainly for financial leasing market in China and the special case of the listed company, in this paper, we spent a lot of space to determine the various parameters of KMV model in the first place, then the selection of a certain number of listed companies financing lease and ST,*ST samples for comparing the empirical analysis, based on the identified model parameters, through a variety of data collection, sorting, processing and analysis, and then by use of Excel of iterative calculation and Matlab to operation parameter variables, eventually have to calculate the expected default rate EDF default distance DD and theory, through the KMV model final output variables to measure the credit risk of financial leasing companies. Through empirical research results show that KMV model can dramatically by default distance DD can identify financial leasing companies, risk differences between ST and*ST companies. Although the theory of expected default rate EDF measure the credit risk degree is not very obvious, the difference between but default distance this variable to identify financing lease company well credit default risk. Investors, creditors, and regulators, and even corporate management can be made by the change of the default distance the most conducive to their corresponding countermeasures, so as to avoid the occurrence of economic loss, default distance value change can be a good early warning utility and prompt action.