Portfolio Optimization and Tail-Risk Analytics of Actively Managed ETFs

Battle Count: 103

Highly relevant to quantitative trading and portfolio management. The paper provides a comprehensive framework for constructing and evaluating portfolios of actively managed ETFs, comparing multiple optimization objectives (mean-variance, CVaR, tangency) under different constraint regimes (long-only, long-short). The dynamic rolling-window allocation design, transaction-cost robustness analysis, and tail-risk diagnostics (Hill estimators, POT-GPD) are directly applicable to systematic portfolio construction, risk management, and strategy evaluation in quantitative trading. The finding that no single optimization rule dominates and that tail-risk varies across strategies is practically important for risk-aware allocation.

Moderate to high complexity. The paper implements multiple optimization frameworks (mean-variance, CVaR, tangency) under two constraint regimes, a dynamic rolling-window rebalancing scheme, bootstrap inference, and several tail-risk diagnostics (Hill estimator, POT-GPD). CVaR optimization requires linear programming or specialized solvers. Dynamic allocation requires careful handling of look-ahead bias. POT-GPD fitting and Hill estimation require threshold selection and diagnostic validation. However, all methods are well-established in the literature and standard optimization libraries (e.g., CVXPY, scipy) can handle the core computations.

The methodology is fully described with explicit formulas for all optimization problems, constraint sets, and tail-risk diagnostics. However, the underlying data is sourced from Bloomberg (proprietary), and no code repository is provided. The 30-fund universe and sample period (Dec 2020–Dec 2025) are clearly specified, enabling partial replication if Bloomberg access is available. Bootstrap procedures and POT-GPD parameter settings are described but exact random seeds are not reported.