金融学院知识库

我国经济快速发展的过程中，绿色金融产品和金融市场也日益完善，为促进绿色、低碳发展提供了强有力的支持。当前，我国的绿色信贷规模已居全球首位，总体上具有规模大、结构优、质量高等特征。但是，由于清洁能源行业初始投资费用高、投资回报周期长等问题，导致其贷款风险测量与控制不够准确，也导致了企业融资困难。因此，本文选择21家国内上市的清洁能源企业和7家传统制造业上市企业作为样本测度其违约风险并对比分析，对我国银行业的绿色违约风险管理起到了借鉴作用。

本文首先对违约风险、现代信用违约风险度量模型以及绿色信贷的含义、信用违约风险、国内外发展概述及其基础理论等进行详细介绍；其次介绍我国绿色信贷的基本情况以及清洁能源产业及其各细分行业的发展态势；然后，由于KMV模型建立在B-S期权定价理论基础上来测算其股权价值及违约点等数据，并由违约距离度量违约风险。但模型当中计算出的股权价值波动率具有“厚尾”的特征。基于这种考虑，本文运用GARCH(1,1)模型测量样本公司的股权价值波动率，并根据修正后的 KMV模型计算清洁能源产业的违约距离，分析三大产业的违约风险情况并与传统制造业比较；之后通过对本文的实证结果进行总结，认为太阳能、风能和氢能三大产业违约风险均高于传统制造业，且其中风能产业的违约概率相对最大，绿色信贷违约风险最高；太阳能产业违约概率变动较小，风险变动较稳定；氢能产业违约概率不断降低，发展前景较好。最后本文从政府、商业银行和企业三个角度提出了针对性的建议。

With the rapid growth of China's economy, green financial products and financial markets have also developed more and more, which has greatly supported the promotion of green growth and low-carbon development. At present, the scale of green credit in China is the first in the world, with the common characteristics of large scale, excellent structure and high quality. However, the high initial investment cost and long investment return cycle of the clean energy industry have led to the lack of accurate measurement and control of its loan risks, which has also led to difficulties in corporate financing. Therefore, this paper selects 21 domestic listed clean energy enterprises and 7 traditional manufacturing listed enterprises as samples to measure their default risks and compare and analyze them, which serves as a guideline for managing green default risks in the Chinese banking sector.

This paper firstly introduces credit risk, modern credit risk metric model and the meaning of green credit, credit risk, overview of domestic and international development and its basic theory in detail; secondly, it introduces the basic status of green credit in China and the development trend of clean energy industry and its various segments; Then, since the KMV model is based on the B-S option pricing theory to measure the equity value and default point, and the credit risk is measured by the default distance. However, the volatility of the equity value calculated in the model has the characteristic of "thick tail". Based on this consideration, the GARCH (1, 1) model is used to correct for stock value volatility of the sample companies, and calculates the default distance of the clean energy industry based on the modified KMV model to calculation the credit risk of the three industries and compare them with the traditional manufacturing industry; then, by summarizing the  empirical results of this paper, we conclude that the credit risk of the solar, wind and hydrogen industries is higher than that of the traditional manufacturing industry. The probability of default in wind energy industry is relatively the highest and the credit risk of green credit is the highest; the probability of default in solar energy industry changes less and the risk changes are more stable; the probability of default in hydrogen energy industry keeps decreasing and the development prospect is better. Finally, this paper concludes with specific recommendations from three perspectives: government, commercial banks and business.