Portfolio Optimization and Tail-Risk Analytics of Actively Managed ETFs

2026-07-03

AI Summary

This study investigates the efficient optimization and tail risk measurement of heterogeneous actively managed ETF portfolios. Leveraging daily data from 30 actively managed ETFs and one fixed-income mutual fund, it systematically evaluates static and dynamic strategies—including mean-variance optimization, CVaR minimization, tangency portfolios, and extreme value theory approaches (Hill estimator and Peaks-Over-Threshold with Generalized Pareto Distribution)—under varying constraints that incorporate dependence structures, dynamic allocation, transaction costs, and multidimensional tail risk metrics. The findings indicate that the tangency portfolio delivers superior cumulative returns and risk-adjusted performance, while a dynamic long-only CVaR-95 strategy proves robustly effective. Despite aggregation, portfolios exhibit pronounced downside tail risk. Innovatively treating actively managed ETFs as a joint opportunity set, this work elucidates how strategy heterogeneity collectively shapes overall portfolio performance.

Abstract

This paper examines portfolio optimization and tail-risk analytics for a heterogeneous universe of actively managed investment funds. Using daily Bloomberg data for 30 funds from 4 December 2020 to 24 December 2025, the study evaluates buy-and-hold, mean--variance, CVaR-based, and tangency-type strategies under long-only and long--short constraints. The sample consists predominantly of actively managed ETFs, with PTTRX retained as an actively managed fixed-income mutual-fund comparator. The results show substantial heterogeneity across thematic equity, fixed-income, income-oriented, multi-asset, and alternative strategies, creating both diversification opportunities and meaningful differences in volatility, drawdown behavior, downside exposure, and tail risk. Historical results indicate that tangency-type portfolios are generally the strongest competitors to the buy-and-hold benchmark in cumulative and risk-adjusted terms, while minimum-variance and CVaR-minimizing portfolios sacrifice upside participation for stronger downside control. Dynamic allocation does not improve all strategies uniformly: the long-only dynamic CVaR-95 portfolio is consistently attractive across several risk-adjusted criteria, whereas long--short dynamic tangency-CVaR portfolios perform strongly but are more sensitive to turnover and implementation costs. Tail-risk diagnostics based on empirical VaR, Expected Shortfall, maximum drawdown, left-tail Hill estimators, and POT--GPD methods show that downside tail exposure remains meaningful after portfolio aggregation. Overall, actively managed ETFs are best evaluated as components of a joint investment opportunity set in which dependence structure, portfolio design, dynamic allocation, implementation frictions, and tail-risk exposure jointly shape performance.

Problem

• Portfolio Optimization
• Tail-Risk Analytics
• Actively Managed ETFs
• Downside Risk
• Risk-Adjusted Performance

Innovation

• CVaR optimization
• tail-risk analytics
• actively managed ETFs
• dynamic portfolio allocation
• extreme value theory