Optimal mean–variance portfolio selection under regime-switching-induced stock price shocks

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Abstract

In this paper, we investigate mean-variance (MV) portfolio selection problems with jumps in a regime-switching financial model. The novelty of our approach lies in allowing not only the market parameters — such as the interest rate, appreciation rate, volatility, and jump intensity — to depend on the market regime, but also in permitting stock prices to experience jumps when the market regime switches, in addition to the usual micro-level jumps. This modeling choice is motivated by empirical observations that stock prices often exhibit sharp declines when the market shifts from a “bullish” to a “bearish” regime, and vice versa. By employing the completion-of-squares technique, we derive the optimal portfolio strategy and the efficient frontier, both of which are characterized by three systems of multi-dimensional ordinary differential equations (ODEs). Among these, two systems are linear, while the first one is an ℓ-dimensional, fully coupled, and highly nonlinear Riccati equation. In the absence of regime-switching-induced stock price shocks, these systems reduce to simple linear ODEs. Thus, the introduction of regime-switching-induced stock price shocks adds significant complexity and challenges to our model. Additionally, we explore the MV problem under a no-shorting constraint. In this case, the corresponding Riccati equation becomes a 2ℓ-dimensional, fully coupled, nonlinear ODE, for which we establish solvability. The solution is then used to explicitly express the optimal portfolio and the efficient frontier.

Original languageEnglish
Article number106200
Pages (from-to)1-8
Number of pages8
JournalSystems and Control Letters
Volume204
DOIs
Publication statusPublished - Oct 2025

Keywords

  • Mean-variance
  • Multi-dimensional nonlinear ODEs
  • Multi-dimensional Riccati equation
  • No-shorting constraint
  • Regime-switching jump shocks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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