使用状态切换预测框架的多期投资组合优化
Multi-Period Portfolio Optimisation Using a Regime-Switching Predictive Framework
AI总结：
本文将 KMRF 状态预测与 MPC 多期投资组合优化结合，并用 Kalman 滤波提升收益估计，提出仅做多的 KMRF+MPC 模型且表现优于行业基准。
摘要：
状态切换给投资组合经理既带来问题，也带来机遇。如果市场行为的切换未能被迅速发现，它就可能成为亏损来源，因为既有交易头寸在新状态下可能不再合适。然而，如果状态切换能够被迅速发现，尤其是能够提前预测，那么这些市场行为变化反而可以成为可观利润的来源。本文工作建立在作者此前两项研究的基础上：第一项研究处理状态检测，第二项是前者的扩展，处理状态预测。具体而言，本工作在通过模型预测控制（MPC）实现的多期投资组合优化框架内，使用我们此前的状态预测模型（KMRF），并通过对 Kalman 滤波器的新颖运用来提升由 KMRF 得出的收益估计的准确性。由此提出的模型被称为 KMRF+MPC 模型，以反映其构成方法；该模型被证明优于行业标准基准，尽管为了被最广泛的投资者接受，它被限制为只能持有多头头寸。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
