Performance and Risk Analytics of Asian Exchange-Traded Funds

TL;DR

The paper investigates risk-return profiles of Asian ETFs by combining traditional mean-variance optimization with tail-risk aware CVaR frontiers on a 29-ETF panel spanning 2014-12-10 to 2025-01-06. It constructs six optimized portfolios (MVP, TVP, M95, M99, T95, T99) using rolling four-year windows and evaluates them against an EQW benchmark under both long-only and long-short with leverage, employing Sharpe, Rachev, and STARR measures and supplementing with Hill tail-index analysis. The findings show systematic optimization consistently improves efficiency over EQW, with EDIV acting as a strong anchor near the tangent portfolio, and moderate leverage (10-20%) boosting returns while higher leverage (30%) increases volatility and drawdowns; tail-risk assessments via CVaR and Hill indices corroborate stronger tail protection for optimized strategies. The study provides practical guidance for constructing diversified, tail-conscious Asian ETF portfolios across emerging and developed markets, while outlining limitations such as trading frictions and sensitivity to rolling-window choices and suggesting avenues for future work in transaction costs, Bayesian estimation, and currency hedging. $95%$ and $99%$ CVaR perspectives are used to stress test tail risk and demonstrate robustness of the optimization framework under extreme market conditions.

Abstract

Investing in Asian markets through exchange-traded funds (ETFs) provides investors with access to rapidly expanding economies and valuable diversification opportunities. This study examines the advantages and challenges of investing in Asian ETFs by conducting comprehensive risk assessments, portfolio analyses, and performance comparisons. The dataset comprises 29 ETFs offering exposure across a wide spectrum of Asian markets, including broad regional funds, country-specific ETFs, as well as sector-focused funds, dividend-oriented ETFs, small-cap portfolios, and emerging market bond ETFs. To evaluate risk and return dynamics, the study employs Markowitz's efficient frontier to identify optimal portfolios for given levels of risk, and conditional value-at-risk (CVaR) to capture potential extreme losses for a more comprehensive risk assessment. Multiple portfolio configurations are analyzed under long-only and long-short investment strategies to assess adaptability across varying market conditions. Furthermore, key performance risk measures, including the Sharpe ratio, Rachev ratio, and stable tail-adjusted return ratio (STARR), are calculated to provide an in-depth evaluation of reward-to-risk efficiency, with particular emphasis on the role of tail behavior in portfolio performance. This research aims to deliver deeper insights into the risk-return trade-offs, tail-risk behavior, and efficiency of Asian ETFs, offering investors a practical foundation for constructing robust and well-diversified portfolios across both emerging and developed Asian markets.

Figures (15)

• Figure 1: Cumulative prices for individual Asian-traded ETFs and EQW, assuming a $100 investment on December 10, 2014.
• Figure 2: The Markowitz efficient frontiers using 1-year (left) and 3-month (right) U.S. Treasury yield
• Figure 3: CVaR efficient frontiers at the 99% and 95% confidence levels using 1-year and 3-month U.S. Treasury yields as the risk-free rate. The top panels correspond to the 99% level with the 1-year (left) and 3-month (right) yields, while the bottom panels show the 95% level for the same yield horizons.
• Figure 4: Cumulative price performance of the optimized portfolios under long-only and long–short strategies from December 2018 to January 2025. The top panels display the long-only strategy (left) and the long–short strategy with 10% leverage (right). The bottom panels correspond to long–short strategies with leverage levels of 20% (left) and 30% (right). Each plot shows the growth of an initial $100 investment on December 11, 2018 over the analysis period.
• Figure 5: Sharpe ratios of the optimized portfolios and EQW under long-only and long–short strategies. Each figure displays results under four configurations: Long–Only (top-left), Long–Short 10% (top-right), Long–Short 20% (bottom-left), and Long–Short 30% (bottom-right).