中美两国公司债信用利差动态过程比较研究
|
摘要
选取中美两国2011年1月至2017年4月的公司债和国债月度交易数据,基于SV模型得到两国公司债的信用利差序列,进而对中美两国公司债的信用利差进行时间序列比较分析.实证发现,中国公司债信用利差序列表现出自回归和移动平均特征,而美国公司债信用利差序列则仅呈现自回归特征;在方差结构方面,中国公司债信用利差序列的残差不具有ARCH效应,而美国公司债信用利差序列的残差具有明显的ARCH效应.同时,对中美两国公司债信用利差建立VAR模型并进行脉冲响应分析,发现中美两国信用利差序列的相关性不强,对彼此的冲击的反应均较弱,为债券市场投资者构建跨国市场债券组合来分散信用风险提供决策支持.
This article selects the monthly transaction data of corporate bonds and government bonds from January 2011 to April 2017 in China and the United States. Based on the SV model, we obtain credit spreads for corporate bonds of the two countries. By analyzing the time-series of credit spreads between China and the United States, we find that the corporate bond credit spreads exhibited autoregressive and moving average characteristics while the U.S. corporate debt credit spreads exhibited only autoregressive characteristics;in terms of variance structure, the residuals of Chinese corporate bond credit spreads have no ARCH effect, while the residuals of US corporate bond credit spreads have a significant ARCH effect corporate bonds. At the same time, the establishment of a VAR model for the credit spreads of corporate bonds between China and the United States and an impulse response analysis reveal that the correlation between Chinese corporate bond credit spreads and the United States corporate bond credit spreads is not strong and the responses to each other's impacts are weak. Based on this, it can provide decision support for investors in the bond market by constructing cross-border market bond portfolios to diversify credit risk.
This article selects the monthly transaction data of corporate bonds and government bonds from January 2011 to April 2017 in China and the United States. Based on the SV model, we obtain credit spreads for corporate bonds of the two countries. By analyzing the time-series of credit spreads between China and the United States, we find that the corporate bond credit spreads exhibited autoregressive and moving average characteristics while the U.S. corporate debt credit spreads exhibited only autoregressive characteristics;in terms of variance structure, the residuals of Chinese corporate bond credit spreads have no ARCH effect, while the residuals of US corporate bond credit spreads have a significant ARCH effect corporate bonds. At the same time, the establishment of a VAR model for the credit spreads of corporate bonds between China and the United States and an impulse response analysis reveal that the correlation between Chinese corporate bond credit spreads and the United States corporate bond credit spreads is not strong and the responses to each other's impacts are weak. Based on this, it can provide decision support for investors in the bond market by constructing cross-border market bond portfolios to diversify credit risk.
引文
[1] Batten J A, Hogan W P. Time variation in the credit spreads on Australian Eurobonds[J]. PacificBasin Finance Journal, 2003, 11(1):81-99.
[2] Bierens H, Huang J Z, Kong W. Time-series estimation of aggregate corporate bond credit spreads[R].Penn State University, January 25, 2005.
[3] Krishnan C N V, Ritchken P H, Thomson J B. Predicting credit spreads[J]. Journal of Financial Intermediation, 2008, 19(4):529-563.
[4] Zhou R X, Du S N, Yu M, Yang F M. A study on pricing credit spread option with longstaff-schwartz and GARCH models in Chinese bond market[J]. Journal of Systems Science and Complexity, 2015,28(6):1363-1373.
[5] Wu H, Jiang Y, Ma Y, Zhang B. Credit spread index of fixed income securities in China[J]. Soft Computing, 2017(4):1-6.
[6] Clark E, Baccar S. Modelling credit spreads with time volatility, skewness, and kurtosis[J]. Annals of Operations Research, 2018, 262(2):431-461.
[7]孙克.企业债的信用风险及其动态度量[M].上海:上海远东出版社,2009.
[8]冯宗宪,郭建伟,孙克·企业债的信用价差及其动态过程研究[J].金融研究,2009, 31(7):54-71.
[9]周荣喜,王先良,杜思楠,王永超.基于VAR模型的中国债券市场信用价差预测研究[J].软科学,2013(11):27-37.
[10]刘善存,牛伟宁,周荣喜.基于SV模型的我国债券信用价差动态过程研究[J].管理科学学报,2014,17(3):37-48.
[11]朱世武,陈健恒.交易所国债利率期限结构实证研究[J].金融研究,2003,25(10):63-73.
[12] Gimeno R, Nave J M. A genetic algorithm estimation of the term structure of interest rates[J].Computational Statistics&Data Analysis, 2009, 53(6):2236-2250.
[13]周荣喜,牛伟宁.中国债券信用价差体系研究[M].北京:科学出版社,2017.
[2] Bierens H, Huang J Z, Kong W. Time-series estimation of aggregate corporate bond credit spreads[R].Penn State University, January 25, 2005.
[3] Krishnan C N V, Ritchken P H, Thomson J B. Predicting credit spreads[J]. Journal of Financial Intermediation, 2008, 19(4):529-563.
[4] Zhou R X, Du S N, Yu M, Yang F M. A study on pricing credit spread option with longstaff-schwartz and GARCH models in Chinese bond market[J]. Journal of Systems Science and Complexity, 2015,28(6):1363-1373.
[5] Wu H, Jiang Y, Ma Y, Zhang B. Credit spread index of fixed income securities in China[J]. Soft Computing, 2017(4):1-6.
[6] Clark E, Baccar S. Modelling credit spreads with time volatility, skewness, and kurtosis[J]. Annals of Operations Research, 2018, 262(2):431-461.
[7]孙克.企业债的信用风险及其动态度量[M].上海:上海远东出版社,2009.
[8]冯宗宪,郭建伟,孙克·企业债的信用价差及其动态过程研究[J].金融研究,2009, 31(7):54-71.
[9]周荣喜,王先良,杜思楠,王永超.基于VAR模型的中国债券市场信用价差预测研究[J].软科学,2013(11):27-37.
[10]刘善存,牛伟宁,周荣喜.基于SV模型的我国债券信用价差动态过程研究[J].管理科学学报,2014,17(3):37-48.
[11]朱世武,陈健恒.交易所国债利率期限结构实证研究[J].金融研究,2003,25(10):63-73.
[12] Gimeno R, Nave J M. A genetic algorithm estimation of the term structure of interest rates[J].Computational Statistics&Data Analysis, 2009, 53(6):2236-2250.
[13]周荣喜,牛伟宁.中国债券信用价差体系研究[M].北京:科学出版社,2017.