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Betting Against Correlation: Testing Theories of the Low-Risk Effect

Last Updated on 10 February, 2024 by Rejaul Karim

In the intricate landscape of asset pricing, “Betting Against Correlation: Testing Theories of the Low-Risk Effect” by Clifford S. Asness, Andrea Frazzini, Niels Joachim Gormsen, and Lasse Heje Pedersen from AQR Capital Management and the University of Chicago’s Booth School of Business, navigates the terrain of the low-risk effect.

Unveiled on February 8, 2017, with revisions up to June 21, 2018, the paper scrutinizes the drivers behind this effect, probing the dichotomy between leverage constraints and behavioral factors. Introducing the innovative Betting Against Correlation (BAC) factor, the study rigorously dissects the interplay of leverage constraints and lottery demand, shedding light on the multifaceted nature of low-risk phenomena.

This exploration goes beyond conventional measures, intertwining volatility, correlation, and idiosyncratic risk, ultimately providing insights into the intricate dynamics of asset pricing, leverage constraints, and sentiment influences.

Abstract Of Paper

We test whether the low-risk effect is driven by (a) leverage constraints and thus risk should be measured using beta vs. (b) behavioral effects and thus risk should be measured by idiosyncratic risk. Beta depends on volatility and correlation, where only volatility is related to idiosyncratic risk. We introduce a new betting against correlation (BAC) factor that is particularly suited to differentiate between leverage constraints vs. lottery explanations. BAC produces strong performance in the US and internationally, supporting leverage constraint theories. Similarly, we construct the new factor SMAX to isolate lottery demand, which also produces positive returns. Consistent with both leverage and lottery theories contributing to the low-risk effect, we find that BAC is related to margin debt while idiosyncratic risk factors are related to sentiment.

Original paper – Download PDF

Here you can download the PDF and original paper of Betting Against Correlation: Testing Theories of the Low-Risk Effect.

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Author

Clifford S. Asness
AQR Capital Management, LLC

Andrea Frazzini
AQR Capital Management, LLC

Niels Joachim Gormsen
University of Chicago – Booth School of Business

Lasse Heje Pedersen
AQR Capital Management, LLC; Copenhagen Business School – Department of Finance; New York University (NYU); Centre for Economic Policy Research (CEPR)

Conclusion

In conclusion, the examination of the low-risk effect delves into its underlying drivers, considering both leverage constraints and behavioral influences. Introducing the betting against correlation (BAC) factor to discern between these factors, we find robust performance in both US and international markets, emphasizing the significance of leverage constraints in shaping the low-risk effect.

Simultaneously, the creation of the SMAX factor, isolating lottery demand, reinforces the nuanced nature of this phenomenon.

The observed intricate relationships, such as the connection between BAC and margin debt, and the correlation of idiosyncratic risk factors with sentiment, underscore the intricate interplay of leverage constraints and behavioral dynamics in influencing the observed low-risk effect.

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FAQ

Q1: What is the low-risk effect, and how does this study contribute to understanding its underlying drivers?

The low-risk effect refers to the phenomenon where low-risk assets outperform high-risk assets. This study contributes by investigating whether the low-risk effect is driven by leverage constraints or behavioral factors. By introducing the innovative Betting Against Correlation (BAC) factor, the authors rigorously examine the interplay between these elements and provide insights into the nuanced dynamics of the low-risk effect.

Q2: What is the Betting Against Correlation (BAC) factor, and how does it differentiate between leverage constraints and lottery demand in explaining the low-risk effect?

The BAC factor is introduced to differentiate between leverage constraints and lottery demand as potential drivers of the low-risk effect. It allows the study to discern whether the phenomenon is influenced more by constraints on leverage or behavioral factors related to lottery demand. The strong performance of the BAC factor in both US and international markets supports the significance of leverage constraints in shaping the observed low-risk effect.

Q3: How does this study go beyond conventional measures in examining the low-risk effect, and what intricate relationships are observed in the analysis?

This study goes beyond conventional measures by intertwining volatility, correlation, and idiosyncratic risk in its examination of the low-risk effect. The observed intricate relationships, such as the connection between the BAC factor and margin debt, and the correlation of idiosyncratic risk factors with sentiment, highlight the complex interplay of leverage constraints and behavioral dynamics in influencing the low-risk effect.

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