Filtering market signals: dynamic asset allocation with momentum and hidden mean reversion
研究了当资产收益呈现短期动量但长期均值回归时，如何利用卡尔曼滤波估计不可观测的长期均值，并推导出最优动态资产配置策略，发现部分信息策略更平滑且优于简单基准。
We study dynamic asset allocation when returns display short-run momentum yet revert to a hidden long-run mean. We extend the two-factor specification of [Koijen, R.S., Rodriguez, J.C. and Sbuelz, A., Momentum and mean reversion in strategic asset allocation. Manage. Sci., 2009, 55, 1199–1213.] into a partially observable linear-Gaussian economy. We estimate the unobservable drift of the return process with a Kalman–Bucy filter and exploit the separation principle to characterize the optimal portfolio and its value under partial information. The optimal weight splits into a myopic momentum bet, an intertemporal hedge, and an information-hedging component that scales with the filter's conditional error variance. Closed-form expressions for the indifference value of information show that the premium for perfect drift observability rises with the noise-to-signal ratio. A simulation study enables us to interpret our theoretical results, and a real data application reveals that the partial-information strategy behaves more smoothly, yet still outperforms a naïve benchmark.