Research on China's Green Finance Credit Risk Measurement Based on Improved KMV Model — Credit risk assessment of new energy automobile industry

Yajun Nie

doi:10.54691/bcpbm.v27i.1975

Authors

Yajun Nie

DOI:

https://doi.org/10.54691/bcpbm.v27i.1975

Keywords:

green finance, credit risk, EGARCH-M model, KMV model

Abstract

This paper focuses on the issue of green finance credit risk measurement, taking China's new energy vehicle listed companies as a sample, taking into account the actual situation of China's financial market, using a modified KMV model to estimate the default distance of selected companies, and comparing with the traditional industries in these three years. The results show that the average default distance of 30 new energy automobile companies is larger than that of 30 traditional automobile manufacturing companies, and the default probability is smaller. As the credit loan risk for the new-energy automobile industry is lower, commercial banks are encouraged to carry out green credit business, and policy and subsidy support related to the new-energy industry shall be given at the same time.

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References

He Qian. Origin, Development and Global Practice of Green Finance [J]. Journal of Southwest University (Social Science Edition), 2021, 47 (01): 83-94 226. DOI: 10.13718/j.cn ki. xdsk. 2021.01.00 8.

Kurbat M , Korbalev I . Methodology for Testing the Level of the EDF Credit Measure. 2010.

Takezawa N , Takezawa N . A Note on Credit Risk of Vertical Keiretsu Firms: Preliminary Evidence from the Japanese Automobile Industry[J]. Asia-Pacific Financial Markets, 2003, 10(4):377-398.

Crosbie P , Bohn J . Modeling default risk[M].

Duffie D , Saita L , Ke W . Multi-period corporate default prediction with stochastic covariates[J]. Journal of Financial Economics, 2006, 83(3):635-665.

Patel K , Vlamis P . An Empirical Estimation of Default Risk of the UK Real Estate Companies[J]. Social Science Electronic Publishing.

Dionysiou D , Lambertides N , Charitou A , et al. An alternative model to forecast default based on Black-Scholes-Merton model and a liquidity proxy[J]. 2008.

LEE W C. Redefinition of the KMV model’s Optimal Default Point Based on Genetic Algorithms: Evidence from Taiwan [J]. Expert Systems with Applications,2011,38 ( 8 ) : 10107 - 10119.

Wang Q., Chen J. Research on Credit Risk Pricing Methods and Models [J]. Modern Finance and Economics - Journal of Tianjin University of Finance and Economics, 2002 (04): 14-16. DOI: 10.19559/j.cn KI. 12-1387. 2002.04.00 4.

Ma Ruowei. KMV Model Applied to Empirical Test of Financial Distress Early Warning of China's Listed Companies [J]. Mathematical Statistics and Management, 2006 (05): 593-601. DOI: 10.13860/j.cnki.sl. 2006.05.05.05.

Jiang J., Gao Y. Research on Credit Risk Assessment of Chinese Listed Companies Based on KMV Model [J]. Journal of Central University of Finance and Economics, 2015 (09): 38-45.

Jiang Shubin. Study on Credit Risk of Commercial Banks Based on Dynamic Default Distance of KMV [J]. Finance and Economy, 2016 (05): 61-65 27. DOI: 10.19622/j.cnki.cn 36-1005/f. 2016.05.02.

Yu J. Financial Development Research, 2017 (08): 34 – 40. DOI: 10.19647/jki .cn. 37-1462/f. 201

Zhang Jiantong, Zhang Min, Guo Zhuoqi. Risk Analysis of Automobile Supply Chain Finance Based on Modified KMV Model [J]. Industrial Engineering and Management, 2018, 24 (01): 128-135 143. DOI: 10.19495/j.cn ki. 1007-5429.2019.01.01 7.

Zhang Binbin, He Dexu. Financial Dominance Decentralization, Recessive Decentralization and Regional Financial Risk: An Empirical Study Based on Kmv and Spatial Panel Dubin Model [J]. Fujian Forum (Human and Social Sciences), 2019 (05): 42 – 53.

Guo Pengfei. Problems and Countermeasures in the Development of Green Credit: Taking Chifeng Area as an Example [J]. Northern Finance, 2022 (01): 81-83. DOI: 10.16459/j.cn KI. 15-1370/f. 2022.01.07.

Liu Qingfu, Chen Zhiwei, He Chang. Assessment and Monitoring of Green Credit Risks in China from the Perspective of New Energy Automotive Industry [J]. Fudan Academic Journal (Social Science Edition), 2020, 62 (02): 192-200.