초록
영어
This study examines the performance of regime-based portfolio rebalancing strategies that utilize hidden Markov models (HMMs) to dynamically adjust asset allocations in response to volatility regime shifts. Based on a globally diversified portfolio reflecting the investment context of institutional investors with significant cross-border exposure, the analysis yields three key findings. First, regime-based strategies consistently outperform static strategic asset allocation (SAA) and buy-and-hold approaches in terms of risk-adjusted returns, while effectively reducing downside risk measures such as conditional value-at-risk (CVaR), maximum drawdown (MDD), and volatility. Second, the benefits of regime-based strategies are more pronounced at higher adjustment intensities, confirming their adaptability under shifting market conditions. Third, the performance advantages of regime-based strategies persist even after incorporating transaction costs. By integrating regime-based signals into the rebalancing process, this study provides empirical implications for institutional investors managing globally diversified portfolios in volatility-sensitive environments.
목차
Ⅰ. Introduction
Ⅱ. Literature Review
Ⅲ. Empirical Findings
1. Descriptive Statistics and Correlation Analysis
2. Hidden Markov Model (HMM) for Volatility Regime Detection
3. Portfolio Characteristics Across Risk Adjustment Levels
4. Performance Comparison of Regime-Based and Benchmark Strategies
5. Performance Comparison: Regime-Based vs. Traditional Rebalancing Strategies
Ⅳ. Conclusion
References
