Dynamic allocation: extremes, tail dependence, and regime Shifts

ArXiv ID: 2506.12587 “View on arXiv”

Authors: Yin Luo, Sheng Wang, Javed Jussa

Abstract

By capturing outliers, volatility clustering, and tail dependence in the asset return distribution, we build a sophisticated model to predict the downside risk of the global financial market. We further develop a dynamic regime switching model that can forecast real-time risk regime of the market. Our GARCH-DCC-Copula risk model can significantly improve both risk- and alpha-based global tactical asset allocation strategies. Our risk regime has strong predictive power of quantitative equity factor performance, which can help equity investors to build better factor models and asset allocation managers to construct more efficient risk premia portfolios.

Keywords: GARCH-DCC-Copula, Volatility Clustering, Regime Switching, Tail Dependence, Risk Premia, Global Multi-Asset

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced quantitative models including GARCH, DCC, Copulas, and Markov Switching regimes, requiring significant mathematical sophistication; it is also highly data- and implementation-focused, with backtested asset allocation strategies and risk model comparisons on real market data.

  flowchart TD
    A["Research Goal: Predict downside risk & market regime<br>to enhance global tactical asset allocation"]
    B["Data Inputs:<br>Global multi-asset returns<br>Equity factor performance data"]
    C["Methodology:<br>GARCH-DCC-Copula model<br>+ Regime Switching"]
    D["Computational Process:<br>Capture outliers, volatility clustering,<br>& tail dependence; forecast risk regimes"]
    E["Key Outcomes:<br>1. Improved risk/alpha allocation strategies<br>2. Risk regime predicts factor performance<br>3. Efficient risk premia portfolios"]
    
    A --> B
    B --> C
    C --> D
    D --> E