커버드콜 ETF의 위험에 대한 이해와 감마 위험 완화를 위한 기초자산 구성 전략

Understanding the Risks of Covered Call ETFs and Underlying Asset Construction Strategies for Mitigating Gamma Risk

Purpose - This study aims to explain the performance and risk characteristics of covered-call ETFs in an intuitive way that investors can readily understand, thereby broadening their understanding of risk selection. It also proposes alternatives for managing the underlying asset, explicitly accounting for the distinctive risks of covered-call ETFs relative to their benchmark indices. Design/methodology/approach - Using seven KRX covered-call indices from 2019-09-24 to 2025-12-24, we estimate monthly market exposure (delta) via regressions on the KOSPI 200. We interpret the regression residual (alpha) as delta-neutral profit and loss and analyze the gap between implied and realized volatility. We further explore whether replacing the KOSPI 200 underlying with Size–Book-to-Market (5×5) portfolios can mitigate the “gamma effect.” Findings - Delta estimates indicate that weekly covered-call indices exhibit relatively more stable delta, whereas ATM covered-call indices show larger delta variation. Regression alpha is positive for most of the sample but turns negative during sharp rallies, suggesting that, under a delta-hedged interpretation, realized gamma-related costs can outweigh option-premium income (theta). The “Large–High” portfolio delivers the smallest sensitivity of delta to market returns and achieves higher returns than the KOSPI 200, supporting its potential as an underlying alternative to reduce the gamma effect. Finally, regressions on changes in VKOSPI yield significantly negative coefficients for most indices, confirming regime dependence: weekly/OTM designs tend to be favored when volatility rises, while ATM/monthly designs are favored when volatility declines. Research implications or Originality - By comparing domestic covered-call indices and presenting delta dynamics and performance decomposition, this study provides investors with a framework for understanding product behavior and offers practitioners practical alternatives for underlying-index design.