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Estimation Of Default Risk In China's Non-financial Corporate Sector

Posted on:2020-02-11Degree:MasterType:Thesis
Country:ChinaCandidate:F ShanFull Text:PDF
GTID:2439330596981311Subject:National Economics
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Since the global financial crisis in 2008,with the implementation of the government's 4 trillion expansionary monetary policy,the debts of various sectors of China's national economy have accumulated rapidly,leading to a rise in the leverage of the macro sector,which in turn increases the potential risks of national economic and financial stability.China's macro sector's leverage ratio is unevenly distributed,with non-financial corporate sectors being the highest,and households and government sectors relatively low.Since General Secretary Xi Jinping proposed the policy of "three remove,one reduction and one supplement" in 2015,the 2016 Central Economic Work Conference proposed "to reduce the leverage ratio of enterprises as the top priority".Excessive borrowing in non-financial corporate sectors has become a major hidden danger to China's financial stability.The risk of default in China's non-financial sector is worthy of further study.How to assess and calculate the default risk of the corporate sector? Existing literature often starts from the flow data of the flow meter,which leads to the inaccuracy of the risk of default.This paper first makes a structural description of the debt status of China's nonfinancial corporate sector,and then analyzes the changes in the leverage ratio of China's nonfinancial corporates;and then uses the National Bureau of Statistics' "First National Economic Census Main Data Bulletin"," Second National Economic Census Main Data Bulletin"," Third National Economic Census Main Data Bulletin" and the industry macro data of the China Statistical Yearbook have compiled the balance sheet of China's nonfinancial corporate sector for 2004-2017;The Contingent Claims Analysis(CCA)measures the default risk of non-financial corporate sectors in China from 2004 to 2017,and divides the non-financial corporate sector into structural divisions to measure the default risk of each sub-sector.Through the research,this paper finds that: Firstly,the macro-leverage rate of nonfinancial corporates in China has increased by a large margin in the past decade,and the macro-leverage ratio of state-owned enterprises is greater than that of private enterprises,but the return on assets of state-owned enterprises is smaller than that of private enterprises.Secondly,the indicators measured by the contingent claims analysis have implicit asset volatility,default distance and default probability.The relationship is that the greater the volatility of the implicit asset,the greater the default probability and the smaller the default distance.The three indicators visualize the dynamic evolution of risk exposures in the nonfinancial corporate sector.Thirdly,the non-financial corporate sector is subdivided according to the industry,and it is found that the default risks of real estates,construction industry and industry are decreasing in turn;the non-financial corporate sector are classified according to the ownership type,and it is found that the default risk of state-owned enterprises is greater than that of private enterprises.Among enterprises,local state-owned enterprises are larger than central state-owned enterprises.Based on the existing data,this paper makes a more in-depth study of the non-financial corporate sector based on previous research.The contribution of this paper is: firstly use the existing data,adjust the parameters in the preparation process,and re-edit the balance sheet of China's non-financial corporate sector.Moreover,the non-financial corporates have been structurally divided to measure the default risks of various sub-sectors of non-financial corporates.The conclusions drawn are consistent with the policy direction of structural deleverage in China,which provides theoretical support for China's structural deleveraging policy.
Keywords/Search Tags:non-financial corporate, default risk, contingent claims analysis
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