基于MCMC模拟的贝叶斯复合状态信用溢价模型研究  

Bayesian Compound State Credit Spread Models Based on MCMC Simulation

作　　者：朱慧明[1] 郝立亚[1] 虞克明[2] 曾惠芳[1] 李素芳[1] 

机构地区：[1]湖南大学工商管理学院,湖南长沙410082 [2]Brunel大学CARISMA研究中心

出　　处：《中国管理科学》2011年第3期1-10,共10页Chinese Journal of Management Science

基　　金：国家自然科学基金项目重点项目(71031004);国家自然科学基金项目(NSFC70771038);教育部留学回国人员科研启动基金项目(教外司留[2010]609);教育部长江学者与创新团队发展计划项目(IRT0916);湖南省自然科学基金创新群体资助项目(09JJ7002)

摘　　要：针对已有的信用溢价模型只考虑了单一尺度的波动均值回复过程,从而导致的信息损失问题,提出了贝叶斯复合状态信用溢价模型,据此辨析不同尺度的信用溢价波动回复状态。利用不同剩余偿付期的中国企业债信用溢价指数序列,引入了基于混合正态分布的多步MCMC方法对复合状态模型进行贝叶斯分析,研究结果表明:不同剩余偿付期的债券具有不同的异方差水平,均值回复过程可以区分为长期和短期两种趋势,并且分别具有不同的尺度维度;长期回复过程显示了序列的整体波动趋势,短期回复过程更细致地刻画了极值点的影响;与传统模型的比较突出了复合状态模型在拟合效果上的优越性。Traditional models used to describe the dynamic behavior of credit spreads didn＇t concentrate on the different time scales of the mean-reversion process.This stud investigutes this proposition in the Chinese corporate bonds context by proposing a kind of compound state credit model.Based on the mixture normal distribution,a kind of multi-step MCMC simulation is used to deal with the model＇s Bayesian inference.On different pay period indexes we find that the credit spreads indices with different pay periods have different structures of heteroscedasticity and the mean-reversion states can include the long and short time scales by using compound state credit model.The results show that the long scale process captures the trends,while the short one captures the extreme events.At last,the superiority of compound state credit model is illustrated by being compared with the classical mean-reversion model.

关 键 词：信用溢价 均值回复 复合状态 贝叶斯推断 MARKOV链 

分 类 号：F830.91[经济管理—金融学]