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论文编号：	8503
作者编号：	2120142502
上传时间：	2016/6/14 13:11:22
中文题目：	我国市政债券主体信用风险评价方法研究
英文题目：	Research on the Evaluation Method of the Credit Risk of Municipal Bonds’ Issuers in China
指导老师：	戚安邦
中文关键字：	市政债券；信用风险评价；模糊层次分析法；改进熵权法
英文关键字：	Municipal bonds; credit risk evaluation; fuzzy analytic hierarchy process (FAHP); improved entropy method
中文摘要：	2014年财政部通过了《2014年地方政府债券自发自还试点办法》对地方政府自发自还地方政府债券进行了相关的规定，并且将上海市、浙江省、广东省、深圳市等10个市、省、自治区或者直辖市作为自发自还地方政府债券的试点地区。目前其他地方政府债券的发行仍由财政部代发，发债额度在当年财政部代发地方政府债的总盘子内。可以预见，除以上10个试点地区之外，以后会有更多的地方政府可以自发自还地方政府债券的自主权。地方政府债券又可以称为市政债券，政府作为债券的发行主体，其信用评价在债券发行过程中具有十分重要的作用，主要体现为两个方面，一是规范市政债券的发行及其在二级市场上的交易，其次是可以推动诚信政府的建设。 本文从区域宏观经济环境、当地政府财政状况、当地政府债务水平、当地政府治理能力以及上级政府的财政支持力度五个纬度构建市政债券主体信用评价指标体系，每一个方面又具体细分为各自的二级指标以及三级指标，最终构建了市政债券主体的信用风险评价指标体系，利用模糊层次分析法对一级指标进行赋权，改进的熵权法对三级指标赋权，将相对应的三级指标的权重之和作为二级指标的权重，最终得到指标体系的完整权重。这种综合主观赋权法以及客观赋权法的赋权方法既能使指标权重符合评价者的主观偏好，又考虑到了指标数据的离散程度以及相关程度，使得本研究的权重确定较为科学全面。 最后，本研究依据指标数据的分布确定了各个指标值所对应的客观打分方法，对全国30个省级行政单位进行信用风险评价的实证分析，评价结果显示天津、上海、江苏、广东以及浙江等地的信用风险综合评价值最高，按照经济区域分类信用风险评价结果表明在综合评价结果上按东部、东北部、西部和中部递减，并且各个经济区域内的离散程度相当，其变异系数都在0.066左右。最后重点对天津市的信用风险进行案例研究，结果显示天津市的地方债务状况以及地方政府治理能力方面非常优秀，相比来说地方财政状况欠佳，主要体现在财政管理水平这个二级指标上。
英文摘要：	In 2014, the ministry of finance adopted the “2014 local government bonds’ issue and reimbursement pilot approaches”, in which some provisions in issue and reimbursement of local government bonds had been made. Shanghai City, Zhejiang Province, Guangdong Province, Shenzhen and other 10 cities, provinces, autonomous regions were selected as pilot areas whom would issues and pay back their local government bonds all by themselves. So far the other local government bonds were issued by ministry of finance, and its amount is included in the total amount issued by the finance ministry. It can be predicted that in addition to the above 10 pilot areas, there will be more local governments whom are granted the autonomy to issue and pay back bonds all by themselves. Local government bond, also knew as municipal bonds, its subject is the local government, whose credit evaluation plays an important role in bond issuance, which is mainly embodied in two aspects, regulating the bonds issuance in primary market and transactions in secondary market, and promoting the construction of credit government. This research establishes the indicator system of local government credit evaluation from five dimensions: regional macroeconomic environment, local government financial situation, local government debt level, local government governance capability and financial support from superior government. Each dimension is specifically subdivided into corresponding second-classed indicators and third-classed ones. Fuzzy analytical hierarchy process (FAHP) is used to weight the first-classed indicators and an improved entropy-weighting method is used to weight the third-classed ones. The weighting method used in this research which integrate the subjective and objective determine weighting method, not only satisfies the subjective preference of the evaluation person, but also considers the dispersion degree and the correlation degree of indicators, which makes the weight determination more scientific and comprehensive. Eventually, objective scoring method based on distribution of indicators data is made, and with which this research makes an empirical study on credit risk evaluation of 30 provincial administrative units. The results show that the comprehensive credit risk evaluations of Tianjin, Shanghai, Jiangsu, Guangdong and Zhejiang are the highest. The credit risk evaluations classified by economic region show that comprehensive evaluation value is decreased from the Eastern Region, Northeast Region, West Region and Central Region. However the dispersion degree of each economic region is quite with all variation coefficient about 0.066. Finally the research focus on the case study of credit risk evaluation of Tianjin, with the result shows that Tianjin is excellent in the local government debt level and local government governance ability and poor in local financial situation, which mainly reflects in the second-level indicators of financial management level.
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