Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm

ArXiv ID: 2504.20116 “View on arXiv”

Authors: Chung-Han Hsieh, Jow-Ran Chang, Hui Hsiang Chen

Abstract

A common belief is that leveraged ETFs (LETFs) suffer long-term performance decay due to \emph{“volatility drag”}. We show that this view is incomplete: LETF performance depends fundamentally on return autocorrelation and return dynamics. In markets with independent returns, LETFs exhibit positive expected compounding effects on their target multiples. In serially correlated markets, trends enhance returns, while mean reversion induces underperformance. With a unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies, we demonstrate that return dynamics – including return autocorrelation, volatility clustering, and regime persistence – determine whether LETFs outperform or underperform their targets. Empirically, using about 20 years of SPDR S&P~500 ETF and Nasdaq-100 ETF data, we confirm these theoretical predictions. Daily-rebalanced LETFs enhance returns in momentum-driven markets, whereas infrequent rebalancing mitigates losses in mean-reverting regimes.

Keywords: Leveraged ETFs, Volatility Drag, Return Autocorrelation, Regime Switching, Rebalancing Frequency, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks including AR(1), AR-GARCH, regime switching, and continuous-time models, supported by a formal theoretical derivation in Theorem 2.1. Empirically, it validates predictions using 20 years of high-frequency data (SPY and QQQ) and discusses practical rebalancing strategies, demonstrating substantial backtest-ready rigor.

  flowchart TD
    A["Research Goal:<br>Challenge Volatility Drag Paradigm<br>for Leveraged ETFs"] --> B["Methodology:<br>AR(1), AR-GARCH, Regime Switching<br>with Flexible Rebalancing"]
    B --> C["Data/Inputs:<br>20 Years of SPY & QQQ<br>Historical Data"]
    C --> D["Computational Process:<br>Simulate LETF Performance<br>vs. Target Returns"]
    D --> E{"Key Findings:<br>Return Dynamics Dictate Outcome"}
    E --> F["Momentum Markets:<br>Positive Autocorrelation<br>Daily Rebalancing Outperforms"]
    E --> G["Mean-Reverting Markets:<br>Negative Autocorrelation<br>Infrequent Rebalancing Mitigates Loss"]