Downside Risk Reduction Using Regime-Switching Signals: A Statistical Jump Model Approach

ArXiv ID: 2402.05272 “View on arXiv”

Authors: Unknown

Abstract

This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identification, a recently developed robust model that distinguishes itself from traditional Markov-switching models by enhancing regime persistence through a jump penalty applied at each state transition. Our JM utilizes a feature set comprising risk and return measures derived solely from the return series, with the optimal jump penalty selected through a time-series cross-validation method that directly optimizes strategy performance. Our empirical analysis evaluates the realistic out-of-sample performance of various strategies on major equity indices from the US, Germany, and Japan from 1990 to 2023, in the presence of transaction costs and trading delays. The results demonstrate the consistent outperformance of the JM-guided strategy in reducing risk metrics such as volatility and maximum drawdown, and enhancing risk-adjusted returns like the Sharpe ratio, when compared to both hidden Markov model-guided strategy and the buy-and-hold strategy. These findings underline the enhanced persistence, practicality, and versatility of strategies utilizing JMs for regime-switching signals.

Keywords: Regime Switching, Statistical Jump Model, Markov Switching, Portfolio Optimization, Risk Mitigation, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper uses advanced statistical models (statistical jump models) and time-series cross-validation, indicating high mathematical complexity, while also conducting a robust out-of-sample backtest with transaction costs and real market data across multiple indices, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal:<br>Reduce downside risk via<br>regime-switching signals"] --> B["Key Methodology:<br>Statistical Jump Model<br>vs. HMM vs. Buy & Hold"]
    A --> C["Data/Inputs:<br>US, DE, JP Indices<br>1990-2023<br>Transaction Costs & Delays"]
    B --> D["Computational Process:<br>1. Feature extraction<br>2. Optimize jump penalty<br>3. Simulate trading"]
    C --> D
    D --> E["Key Findings:<br>JM strategy outperforms<br>• Lower Vol & Max Drawdown<br>• Higher Sharpe Ratio<br>• Enhanced Persistence"]