发表机构
Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对银行国库券组合在利率风险和交易成本下的多期优化问题，提出基于动态Nelson-Siegel和马尔可夫决策过程的模拟框架，通过逆向归纳求解最优策略，并量化截断误差的影响。
AI 中文摘要
银行国库券组合必须在不同期限的债券之间平衡收益、流动性和利率风险。静态配置规则不适合此任务：集中于长期证券且没有动态调整机制的组合，在利率上升时可能累积大量按市值计价的损失和流动性压力，正如2023年硅谷银行的失败所示。我们开发了一个基于模拟的可处理框架，用于在利率风险和比例交易成本下进行多期债券组合优化。收益率曲线动态使用动态Nelson-Siegel参数化建模，并采用向量自回归因子动态，从中我们构建了一个时间非齐次的离散状态马尔可夫链，近似跨债券期限的联合收益率过程。该链构成了有限期马尔可夫决策过程的状态空间，其中投资者在比例再平衡成本约束下最大化预期终期财富。最优组合策略通过逆向归纳获得。我们还量化了截断转移核引入的近似误差，并表明它使平均终期财富几乎不变，同时显著扭曲回撤和尾部统计。
英文摘要
Bank treasury portfolios must balance yield, liquidity, and interest-rate risk across bonds of different maturities. Static allocation rules are ill-suited to this task: portfolios concentrated in long-duration securities with no dynamic adjust- ment mechanism can accumulate large mark-to-market losses and liquidity stress under rising interest rates, as illustrated by the failure of Silicon Valley Bank in 2023. We develop a tractable simulation-based framework for multi-period bond port- folio optimization under interest-rate risk and proportional transaction costs. Yield-curve dynamics are modeled using the Dynamic Nelson-Siegel parameter- ization with Vector Autoregressive factor dynamics, from which we construct a time-inhomogeneous discrete-state Markov chain approximating the joint yield process across bond maturities. This chain forms the state space of a finite- horizon Markov Decision Process in which the investor maximizes expected terminal wealth subject to proportional rebalancing costs. The optimal portfolio policy is obtained by backward induction. We also quantify the approximation error introduced by truncating the transition kernel, and show that it leaves mean terminal wealth almost unchanged while substantially distorting drawdown and tail statistics.
