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本文选取上海证券交易所2003年至2010年国债和企业债交易数据,通过遗传算法求解五因子利率期限结构模型,分别得到不同期限国债和企业债的收益率时间序列,然后分别用两因子与四因子仿射过程来刻画国债收益率与企业债收益率的动态变化,从而构建债券信用价差的两因子仿射期限结构模型,并运用卡尔曼滤波法求解模型参数,进而预测出企业债信用价差序列.实证结果表明,该仿射模型能够较好地描述企业债信用价羞的动态变化,可为企业债及其衍生产品的定价提供方法支持.
This paper chooses the transaction data of government bonds and corporate bonds from 2003 to 2010 in Shanghai Stock Exchange, and uses the genetic algorithm to solve the five-factor interest rate term structure model, obtains the time series of yields of government bonds and corporate bonds with different maturities, and then respectively uses two factors and four Factor affine process to characterize the dynamic changes of the bond yields and corporate bond yields to build a two-factor affine term structure model of bond credit spreads, and use Kalman filter to solve the model parameters, and then predict the corporate bond credit spread sequence The empirical results show that the affine model can well describe the dynamic changes of credit risk of corporate bonds, and can provide methodological support for the pricing of corporate bonds and their derivatives.