伴随着人民币国际化和国内金融市场深层次建设的加速,债券市场和利率互换市场都越来越受到多元主体的关注。利率债市场是公开利率的风向标之一,利率互换市场则是金融机构风险管理的重要抓手,两个市场受同样的因素影响具有相似的反应机制...伴随着人民币国际化和国内金融市场深层次建设的加速,债券市场和利率互换市场都越来越受到多元主体的关注。利率债市场是公开利率的风向标之一,利率互换市场则是金融机构风险管理的重要抓手,两个市场受同样的因素影响具有相似的反应机制,彼此之间存在着复杂的动态相依关系,刻画这种关系对于完善金融市场、丰富政策工具和强化风险管理都有重要意义。本文采用t-Copula模型和DCC-GARCH模型,基于一年期和五年期国债收益率、互换利率收益率数据讨论了我国利率债和利率互换之间的动态相依关系和波动溢出效应。本文发现两者之间不仅存在着动态相依关系,还具有显著的交互影响和波动溢出效应。With the acceleration of the internationalization of the Renminbi and the deepening construction of domestic financial markets, both the bond market and the interest rate swap market are increasingly garnering attention from diverse entities. The interest rate bond market serves as one of the barometers for public interest rates, while the interest rate swap market is a crucial mechanism for risk management in financial institutions. Both markets, influenced by similar factors, exhibit akin reaction mechanisms and share a complex dynamic interdependence. Characterizing this interdependence is vital for the refinement of financial markets, the enrichment of policy tools, and the strengthening of risk management. This paper employs the t-Copula model and the DCC-GARCH model to discuss the dynamic interdependence and volatility spillover effects between China’s interest rate bonds and interest rate swaps, using data on one-year and five-year government bond yields and swap rate returns. The findings reveal not only a dynamic interdependence between the two but also significant mutual influences and volatility spillover effects.展开更多
利率期限结构及相关问题一直是金融学的研究热点。文章借助于动态Svensson模型、深度学习,构建了国债利率期限结构的深度学习预测模型。选取2012年1月~2024年5月银行间零息国债收益率数据,对其进行了实证分析,并与DS模型、加入宏观经济...利率期限结构及相关问题一直是金融学的研究热点。文章借助于动态Svensson模型、深度学习,构建了国债利率期限结构的深度学习预测模型。选取2012年1月~2024年5月银行间零息国债收益率数据,对其进行了实证分析,并与DS模型、加入宏观经济变量的DS模型、不加入宏观经济变量的深度学习模型的预测结果进行了对比,结果显示文章提出的深度学习预测模型效果显著提升,鲁棒性更好。The term structure of interest rates and related issues has always been a research hotspot in finance. With the help of the dynamic Svensson model and deep learning, this paper constructs a deep learning prediction model for the term structure of treasury bond interest rates. This paper selects the yield data of inter-bank zero-interest treasury bond bonds from January 2012 to April 2024 to conduct an empirical analysis. The results show that compared with the prediction results of the DS model, the DS model with macroeconomic variables, and the deep learning model without macroeconomic variables, the deep learning prediction model proposed in this paper has significantly improved its effectiveness and has better robustness.展开更多
文摘伴随着人民币国际化和国内金融市场深层次建设的加速,债券市场和利率互换市场都越来越受到多元主体的关注。利率债市场是公开利率的风向标之一,利率互换市场则是金融机构风险管理的重要抓手,两个市场受同样的因素影响具有相似的反应机制,彼此之间存在着复杂的动态相依关系,刻画这种关系对于完善金融市场、丰富政策工具和强化风险管理都有重要意义。本文采用t-Copula模型和DCC-GARCH模型,基于一年期和五年期国债收益率、互换利率收益率数据讨论了我国利率债和利率互换之间的动态相依关系和波动溢出效应。本文发现两者之间不仅存在着动态相依关系,还具有显著的交互影响和波动溢出效应。With the acceleration of the internationalization of the Renminbi and the deepening construction of domestic financial markets, both the bond market and the interest rate swap market are increasingly garnering attention from diverse entities. The interest rate bond market serves as one of the barometers for public interest rates, while the interest rate swap market is a crucial mechanism for risk management in financial institutions. Both markets, influenced by similar factors, exhibit akin reaction mechanisms and share a complex dynamic interdependence. Characterizing this interdependence is vital for the refinement of financial markets, the enrichment of policy tools, and the strengthening of risk management. This paper employs the t-Copula model and the DCC-GARCH model to discuss the dynamic interdependence and volatility spillover effects between China’s interest rate bonds and interest rate swaps, using data on one-year and five-year government bond yields and swap rate returns. The findings reveal not only a dynamic interdependence between the two but also significant mutual influences and volatility spillover effects.
文摘利率期限结构及相关问题一直是金融学的研究热点。文章借助于动态Svensson模型、深度学习,构建了国债利率期限结构的深度学习预测模型。选取2012年1月~2024年5月银行间零息国债收益率数据,对其进行了实证分析,并与DS模型、加入宏观经济变量的DS模型、不加入宏观经济变量的深度学习模型的预测结果进行了对比,结果显示文章提出的深度学习预测模型效果显著提升,鲁棒性更好。The term structure of interest rates and related issues has always been a research hotspot in finance. With the help of the dynamic Svensson model and deep learning, this paper constructs a deep learning prediction model for the term structure of treasury bond interest rates. This paper selects the yield data of inter-bank zero-interest treasury bond bonds from January 2012 to April 2024 to conduct an empirical analysis. The results show that compared with the prediction results of the DS model, the DS model with macroeconomic variables, and the deep learning model without macroeconomic variables, the deep learning prediction model proposed in this paper has significantly improved its effectiveness and has better robustness.
