我国中期票据信用利差的影响因素及预测研究

发布时间：2018-04-19 20:32

本文选题：中期票据 + 信用利差　；参考：《江西财经大学》2017年硕士论文

【摘要】：中期票据自2008年在我国银行间债券市场正式发行流通以来,以一种后来者居上的态势扮演着市场中越来越重要的融资工具角色。中期票据与所有的信用债一样,存在信用利差现象,即为了弥补投资者承担中期票据发行方违约的风险而高于无风险利率的利差。因此,信用利差可作为投资者的一种手段来推断中期票据的信用风险情况,从而做出最有利的投资决策。在学术界,针对信用债存在的信用利差进行过大量的研究,但尚未得出一个统一的结论。研究中期票据信用利差的影响因素及其影响的过程,将对投资者在进行投资品种、投资时点等方面的选择提供帮助;同时,有助于监管层制定出一个被市场接受的指导价格,从而有助于使我国信用债交易更加顺畅。目前,对于国内中期票据信用利差的相关学术研究很少,更缺少在实证研究的基础上得出对我国中期票据市场存在的现实性问题的研究,因此,本文对此进行研究将有助于丰富已有的研究成果。本文首先从我国中期票据的概念、发展历程、风险因素等方面进行了定性分析,然后利用模型对我国中期票据信用利差影响因素进行实证研究,最后利用本文得到的中期票据信用利差影响因素构建多因素模型对信用利差进行预测分析。本文为了达到更全面研究信用利差影响因素的目的,从三个方面系统的总结出有可能影响我国中期票据信用利差的影响因素:一是宏观经济因素,包括进出口增长指标、人民币有效汇率、房地产相关指标、Shibor等;二是中期票据市场自身影响因素,如中期票据的市场流动性、中期票据的供求情况等;三是从结构化模型出发考虑的影响因素,如无风险收益率、利率期限结构指标等。本文选取了中央国债登记结算有限责任公司的AA级、AA+级、AAA级中期票据收益率曲线生成的信用利差作为研究对象,先研究了这三类中期票据信用利差的影响因素,并分析了这些影响因素的影响特点和影响强度,最后在得出具体的中期票据信用利差影响因素的基础上,构建了多因素模型预测中期票据信用利差。实证结果表明,预测值曲线和真实值曲线走势基本保持一致,大部分信用利差预测与实际值偏差较小,达到了较好预测效果。
[Abstract]:Since the official issuance of the medium-term note in the inter-bank bond market in 2008, the medium-term bill has played an increasingly important role in the financing tools in the market. The medium term bill, like all credit bonds, has the phenomenon of credit spreads, that is, to make up for the risk that the investors will take the risk of default of the medium-term bill issuer. It is higher than the margin of risk-free interest rate. Therefore, credit spreads can be used as an investor to deduce the credit risk of medium-term bills and make the most favorable investment decisions. In academic circles, a large number of studies have been made on credit spreads of credit bonds, but a unified conclusion has not been reached. The influence factors of the difference and the process of its influence will help the investors to choose the variety of investment and the time of investment. Meanwhile, it will help the regulators to make a market accepted guiding price, which will help to make the credit transaction of our country more smooth. At present, it is related to the credit spreads of domestic medium term bills. There are few academic studies, and there is a lack of research on the reality of the medium-term bill market in China on the basis of empirical research. Therefore, this study will help to enrich the existing research results. Firstly, this paper makes a qualitative analysis on the concept, the development calendar, the risk factors and so on in the medium term bill in China. This paper uses the model to make an empirical study on the factors affecting the credit spreads of medium-term bills in China. Finally, the paper makes a prediction and analysis of the credit spreads by constructing the multi factor model of the medium-term bill credit spreads. In order to achieve a more comprehensive study of the factors affecting the credit spreads, this paper systematically summarizes the three aspects. The factors that may affect China's medium-term bill credit spreads: one is the macroeconomic factors, including the import and export growth index, the effective exchange rate of RMB, the real estate related index, Shibor and so on; two is the influence factors of the medium-term bill market, such as the market liquidity of medium-term bill, the supply and demand of medium-term bills, and so on; three is from the structured model. In this paper, we select the credit spreads of the AA, AA+ and AAA medium term bill yield curve as the research object, and first study the factors affecting the credit spreads of these three types of medium-term bills, and analyze the factors that influence the credit spreads of these three types of medium-term bills. The impact of these factors and the impact strength, finally on the basis of the specific medium-term bill credit spreads influence factors, the multi factor model is constructed to predict the mid term bill credit spreads. The empirical results show that the trend of the predicted value curve and the real value curve is basically consistent, most of the credit difference prediction and the actual value deviation are deviations. It is smaller and achieves better prediction effect.

【学位授予单位】：江西财经大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F832.2

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