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Study on the Credit Risk Measurement in Listed Companies Based on the Option Pricing Model

Author: WangDongFang
Tutor: FanYuLian
School: North China University of
Course: Applied Mathematics
Keywords: Credit Risk KMV Uncertain volatility Distance to Default
CLC: F832.51
Type: Master's thesis
Year: 2011
Downloads: 50
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Abstract


With the process of economic globalization , risk management has increasingly become a core element which the company management . Credit risk is the most important and oldest risk in the financial markets , the credit risk is the possibility of borrowers for various reasons failed to timely and full repayment of the debt or a bank loan defaulting . Accurately measure the size of the credit risk , minimize losses or maximize profits are very important , after decades of development , has formed various methods to measure credit risk . KMV Company clever use of the idea of option pricing theory to predict the likelihood of default of the listed companies , through the analysis of fluctuations in the price of the shares of listed companies , the model is based on the stock market data , so dynamic with the forward-looking measure of credit risk . For the lack of status of credit institutions and credit data , KMV model is very suitable for the measure of the credit risk of listed companies in China . Paper selected 10 paired ST and non - ST companies using KMV model to measure credit risk , the empirical results show that the The KMV model accurately measure the credit risk of listed companies , and the use of the KMV model of China's media industry , the company risk measure . Factors affecting the distance to default , the most sensitive to the impact of the volatility of the equity value of the distance to default , equity volatility is more severe , then the greater the likelihood of default occurs , the volatility of the equity value of the Company 's research was particularly important. According to the uncertain volatility model , to be given the the volatility σ t specific forms σ t = σ ( the S t < / sub > t ) ( for to determine volatility ) or σ t suitable stochastic differential equations , often there are certain difficulties , but we are usually able to know that it's a change in the scope of [ σ the min , σ < / sub>. Calculated using the historical volatility law σ min and the σ max KMV model , we get σ min default distance and σ < sub > max default distances, resulting in the the default distance range of the listed companies , play a role in warning of the listed company 's credit risk regulation .

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CLC: > Economic > Fiscal, monetary > Finance, banking > China's financial,banking > Financial market
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