Work

Regime-Switching Asset Allocation Using a Framework Combing a Jump Model and Model Predictive Control

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13172837

Authors: Xianglong Li, Jianjun Chen, Xiangxing Tao, Yanting Ji

Institution: Zhejiang University of Science and Technology

Language: English

Topic: Risk and Portfolio Optimization

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Zhejiang University, Zhejiang University of Science and Technology