Regime-Switching Asset Allocation Using a Framework Combing a Jump Model and Model Predictive Control
Xianglong Li, Jianjun Chen, Xiangxing Tao, Yanting Ji
Source abstract
This study proposes a novel hybrid framework that integrates a jump model with model predictive control (JM-MPC) for dynamic asset allocation under regime-switching market conditions. The proposed approach leverages the jump model to identify distinct market regimes while incorporating a rolling prediction mechanism to estimate time-varying asset returns and covariance matrices across multiple horizons. These regime-dependent estimates are subsequently used as inputs for an MPC-based optimization process to determine optimal asset allocations. Through comprehensive empirical analysis, we demonstrate that the JM-MPC framework consistently outperforms an equal-weighted portfolio, delivering superior risk-adjusted returns while substantially mitigating portfolio drawdowns during high-volatility periods. Our findings establish the effectiveness of combining regime-switching modeling with model predictive control techniques for robust portfolio management in dynamic financial markets.
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