Multi-period Dynamic Bond Portfolio Optimization Utilizing a Stochastic Interest Rate Model
Multi-period Dynamic Bond Portfolio Optimization Utilizing a Stochastic Interest Rate Model
复制标题
利用随机利率模型的多周期动态债券投资组合优化
DOI：
10.1007/s10690-023-09401-2
复制
发表时间：
2023
影响因子：
1.7
通讯作者：
Naoki Makimoto
中科院分区：
文献类型：
--
作者：
Yoshiyuki Shimai;Naoki Makimoto
无论资产类别如何，由于理论和结构的复杂性，将多期动态投资组合优化应用于真实的投资活动具有挑战性。在基于随机利率模型的债券投资组合方面，有一些文献关注多期动态债券投资组合优化的理论方面，例如导出最优投资组合的解析解，可以肯定的是，但是没有实证研究分析实际债券市场。此外，迄今为止还没有制定出一种考虑到现实投资限制因素的方法。在本文中，我们提出了一个新的框架，多期动态债券投资组合优化。由于债券收益率可以近似为构成随机利率模型的因素的线性组合，因此我们应用了考虑交易成本的线性再平衡规则，以及自我融资和卖空约束。然后，作为实证分析，通过分析从日本有息国债推算出的贴现债券，进行了投资回测。结果表明，多周期优化代表了一个相对较高的性能相比，单周期优化。此外，当投资期限和投资利用期延长到某一点时，业绩会改善。
Regardless of the asset class, applying multi-period dynamic portfolio optimization to real investment activity is challenging due to theoretical and structural complexities. In terms of a bond portfolio based on a stochastic interest rate model, some literature exists that focuses on theoretical aspects of multi-period dynamic bond portfolio optimization, such as deriving analytical solutions for optimal portfolios, to be sure, but no empirical studies analyzed the actual bond market. Additionally, a methodology that considers realistic investment constraints has not been developed thus far. In this paper, we propose a new framework for multi-period dynamic bond portfolio optimization. As bond return can be approximated by a linear combination of factors that constitute a stochastic interest rate model, we apply linear rebalancing rules that consider transaction costs, in addition to self-financing and short sales constraints. Then, as an empirical analysis, we conduct an investment backtest by analyzing discount bonds estimated from Japanese interest-bearing government bonds. The results indicate that multi-period optimization represents a relatively high performance compared to single-period optimization. Further, the performance improves as the investment horizon and investment utilization period are extended up to a certain point.