2023The Journal of Portfolio ManagementRequires access

Modernizing Volatility-Managed Strategies

Junseung Bae, Ryan Poirier

Open publisher page 2 citations

Abstract

There is a positive relationship between the performance of volatility managed strategies and the accuracy of the volatility estimation—more-accurate forecasts result in higher Sharpe ratios. Industry-standard volatility managed strategies allow a full day between volatility estimation and execution. In other words, we estimate volatility after the close of t − 2, execute the trade market-on-close t − 1, and capture net profits on t. This full-day lag naturally degrades the forecast accuracy, potentially resulting in suboptimal Sharpe ratios. The authors propose a robust framework that shortens the lag, effectively achieving a more accurate forecast by incorporating more-current information in the prediction model. The result is higher Sharpe ratios, higher utility, and lower volatility of volatility.

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What this paper is about

There is a positive relationship between the performance of volatility managed strategies and the accuracy of the volatility estimation—more-accurate forecasts result in higher Sharpe ratios. Industry-standard volatility managed strategies allow a full day between volatility estimation and execution. In other words, we estimate volatility after the close of t − 2, execute the trade market-on-close t − 1, and capture net profits on t. This full-day lag naturally degrades the forecast accuracy, potentially resulting in suboptimal Sharpe ratios. The authors propose a robust framework that shortens the lag, effectively achieving a more accurate forecast by incorporating more-current information in the prediction model. The result is higher Sharpe ratios, higher utility, and lower volatility of volatility.

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Available abstract

There is a positive relationship between the performance of volatility managed strategies and the accuracy of the volatility estimation—more-accurate forecasts result in higher Sharpe ratios. Industry-standard volatility managed strategies allow a full day between volatility estimation and execution. In other words, we estimate volatility after the close of t − 2, execute the trade market-on-close t − 1, and capture net profits on t. This full-day lag naturally degrades the forecast accuracy, potentially resulting in suboptimal Sharpe ratios. The authors propose a robust framework that shortens the lag, effectively achieving a more accurate forecast by incorporating more-current information in the prediction model. The result is higher Sharpe ratios, higher utility, and lower volatility of volatility.

Key concepts: Sharpe ratio, Volatility (finance), Econometrics, Implied volatility, Volatility swap, Economics, Volatility risk premium, Forward volatility

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