基于多阶段随机规划模型的国债动态积极投资策略

本文提出国债组合投资的多阶段随机规划模型,导出基于未来利率市场不确定信息的具备动态调整特点的国债组合主动投资策略。该模型采用基于利率水平、斜率和曲率"三位一体"的离散情景树刻画未来利率期限结构动态演化过程,其中特别考虑了广义货币供给变动的影响;通过最小化国债组合收益的条件风险价值,对国债组合进行主动动态调整;同时兼顾国债投资安全性、流动性和收益性等要求,实现了国债组合投资管理中利率风险规避和收益能力的有效匹配。实证研究表明,与传统久期配比免疫模型相比,该模型确定的最优策略不仅能够为国债组合提供更强的抵御利率风险能力,而且能够稳步提升其收益空间,为金融机构实现国债投资的主动管理提供决策支持。

Multi-stage Stochastic Programming Model for Active and Dynamic Government Bonds Investment Strategies

A multi-stage stochastic programming (MSSP) model for investment in government bond portfolios is proposed in a dynamic setting, an active dynamic strategy under uncertainty is derived. The uncertainty is modeled by discrete scenario trees including the levels, slopes and curves of interest rates, and the growth rate of broad money supply, which reflect the dynamic evolution of the term structure of interest rates. The model minimizes the CVaR of the government bond portfolio with comprehensive consideration of safety, liquidity and profitability, and achieves an effective balance between risk and the expected return. Empirical results show that the MSSP model outperforms traditional duration vector immunization approaches significantly, in terms of stronger competence in profit generation and risk control, which provides a flexible and effective decision support for active management of government bond investment by financial institutions.