Empirical Study Of China’s Industry Credit Risk Based On KMV Model

The global financial crisis triggered by the subprime crisis made the international financial circles to realize that along with the revolution in the financial sector, the development of various financial derivatives, credit risk has become one of the most critical financial risks. In that context, this paper conducts comparative analysis of credit risks in different industries in China through KMV model, which calculates Default Distance (DD) and Extreme-Default-Distance (Ex_DD) to measure credit risks under normal conditions and extreme conditions. In order to improve the estimation accuracy, we select GARCH(1,1) model to estimate the key parameter-fluctuation ratio of stock value, based on the modification of KMV model by domestic scholars. Then we conduct the empirical analysis sampled272Share A listed companies from January1,2004to December31,2009. According to SYWG primary industry classification standard, the selected sample covers8industries, including real estate, automobile, non-ferrous metals, steel, electronics, financial, farming, forestry, animal husbandry and fishery, and energy industries. The result shows that DD and Ex_DD perform well in capturing China’s macro-economic trend when portraying industrial credit risks and they can give out early warning signals when macroeconomic condition worsens. DD may underestimate industrial credit risks when under extreme economic circumstances while Ex_DD may magnify the volatility when estimating industrial credit risks conservatively, which is effective in identifying the differences on credit risks of various industries under extreme economic environment. Non-ferrous metals, electronics, and real estate industries belong to the high-risk category trough DD estimation. The high credit risk of non-ferrous metals and electronic industry is due to their high industry volatility. SteeL, farming, forestry, animal husbandry and fishery industries are less risky according to DD measurement but due to different reasons. Though the steel industry is ranked lower in credit riskiness, problems such as overcapacity, structural transformation covered up by policy support still pose great risk concerns. In extreme economic environment, credit risk indicators fluctuate significantly, exhibiting great risk exposure. Credit risk of auto-industry soared during crisis but showed strong cyclical adjustment while electronics industry demonstrated fester recovery than the entire macro-economy. At last, this paper proposes a application of risk management and profit creation in banks and other financial institutions, using KMV model based on the conclusion mentioned above.