Regime-switching poses both problems and opportunities for portfolio managers. If a switch in the behaviour of the markets is not quickly detected it can be a source of loss, since previous trading positions may be inappropriate in the new regime. However, if a regime-switch can be detected quickly, and especially if it can be predicted ahead of time, these changes in market behaviour can instead be a source of substantial profit. The work of this paper builds on two previous works by the authors, the first of these dealing with regime detection and the second, which is an extension of the first, with regime prediction. Specifically, this work uses our previous regime-prediction model (KMRF) within a framework of multi-period portfolio optimisation, achieved by model predictive control, (MPC), with the KMRF-derived return estimates accuracy-boosted by means of a novel use of a Kalman filter. The resulting proposed model, which we term the KMRF+MPC model, to reflect its constituent methodologies, is demonstrated to outperform industry-standard benchmarks, even though it is restricted, in order to be acceptable to the widest range of investors, to long-only positions.

翻译：机制转换对投资组合管理者而言既是挑战也是机遇。若市场行为转变未能被及时察觉，可能导致损失，因为原有交易头寸在新机制下可能失效。然而，若能迅速识别机制转换，特别是提前预测市场行为的变化，这些转变反而可能成为获取显著收益的源泉。本文在前两项相关研究基础上展开：第一项研究聚焦机制检测，第二项作为前者的延伸，探讨机制预测。具体而言，本文采用我们先前提出的机制预测模型（KMRF），将其融入基于模型预测控制（MPC）的多阶段投资组合优化框架中，并通过卡尔曼滤波的创新应用来增强KMRF衍生收益估计的精确度。最终提出的模型（被命名为KMRF+MPC模型，以反映其构成方法论）即使受限于仅做多头头寸的策略（以确保能被最广泛的投资者接受），仍被证明优于行业标准基准。