Robustness of CVaR-Minimizing Dynamic Allocation: Evidence from Multi-Asset Portfolios Through 2025

Abstract

Traditional risk parity approaches rely largely on volatility measures, which may not fully capture asymmetric risk profiles. This study examines a dynamic allocation approach that minimizes portfolio-level Conditional Value-at-Risk (CVaR). The CVaR-Minimizing Dynamic Allocation (CVaR-DA) approach is intended to manage tail-risk events more effectively than traditional variance-based methods. We conducted an out-of-sample rolling-window simulation for the dynamically re-estimated strategies, covering different market conditions from 2015 to early 2025. Two investment universes were examined: a concentrated equity–gold portfolio and a multi-asset portfolio comprising global equities, sovereign bonds, commodities, and gold. Variance-based DRP generated higher Sharpe ratios than Static Risk Parity in both universes while maintaining low portfolio turnover. The CVaR-DA approach provided better downside protection, particularly in the multi-asset universe, but produced higher turnover. Bootstrap inference yielded positive mean differences in Sharpe ratios between DRP and Static Risk Parity. However, the confidence intervals included zero, indicating that the differences were not statistically significant at conventional levels. The favorable drawdown results nevertheless suggest that dynamic risk allocation may improve portfolio resilience when risk conditions change. Moving from static, volatility-based allocation toward adaptive strategies that account for tail risk may therefore support capital preservation for institutional investors and fund managers.