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Measuring Skewness Premia | an Overview

Last Updated on 10 February, 2024 by Rejaul Karim

In “Measuring Skewness Premia,” Hugues Langlois presents a novel methodology to empirically investigate the roles of systematic and idiosyncratic skewness in explaining expected stock returns.

The paper introduces a comprehensive analysis using a multitude of predictors to forecast the cross-sectional ranks of systematic and idiosyncratic skewness, which are found to be more predictable than their actual values. The study unveils that the forecasts of systematic skewness create a significant spread in ex post systematic skewness, ultimately carrying a substantial risk premium ranging from 6% to 12% per year.

This premium remains robust even when considering various other factors such as downside beta, size, value, momentum, profitability, and investment. In contrast, the role of idiosyncratic skewness in pricing stocks is found to be less consistent.

The paper also delves into the differences in the determinants of systematic and idiosyncratic skewness, providing valuable insights into the complexities of skewness premia.

Abstract Of Paper

We provide a new methodology to empirically investigate the respective roles of systematic and idiosyncratic skewness in explaining expected stock returns. Using a large number of predictors, we forecast the cross-sectional ranks of systematic and idiosyncratic skewness which are easier to predict than their actual values. Compared to other measures of ex ante systematic skewness, our forecasts create a significant spread in ex post systematic skewness. A predicted systematic skewness risk factor carries a significant risk premium that ranges from 6% to 12% per year and is robust to the inclusion of downside beta, size, value, momentum, profitability, and investment factors. In contrast to systematic skewness, the role of idiosyncratic skewness in pricing stocks is less robust. Finally, we document how the determinants of systematic and idiosyncratic skewness differ.

Original paper – Download PDF

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Author

Hugues Langlois
HEC Paris – Finance Department

Conclusion

In conclusion, Hugues Langlois’s research on “Measuring Skewness Premia” introduces a significant methodological advancement in the empirical investigation of systematic and idiosyncratic skewness in relation to expected stock returns.

The study’s use of numerous predictors to forecast cross-sectional ranks of skewness, which are demonstrated to be more predictable than their actual values, provides valuable insights into the dynamics of skewness premia.

The findings reveal the substantial spread in ex post systematic skewness created by the forecasts, with the predicted systematic skewness risk factor carrying a significant and robust risk premium ranging from 6% to 12% per year, even when accounting for various other influential factors.

While the role of idiosyncratic skewness in pricing stocks is found to be less consistent, the research effectively documents the differing determinants of systematic and idiosyncratic skewness.

Overall, this study significantly contributes to the understanding of skewness premia and their implications for stock returns.

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FAQ

What does Hugues Langlois’s research on “Measuring Skewness Premia” contribute to our understanding of expected stock returns?

Langlois’s research introduces a novel methodology to investigate the roles of systematic and idiosyncratic skewness in explaining expected stock returns. The study provides insights into the predictability of skewness, highlighting the dynamics of skewness premia.

How does the research impact our understanding of systematic skewness and its influence on stock returns?

The study reveals that forecasts of systematic skewness significantly impact ex post systematic skewness, carrying a substantial risk premium of 6% to 12% per year. This premium remains robust even when considering factors like downside beta, size, value, momentum, profitability, and investment. In contrast, the role of idiosyncratic skewness in pricing stocks is found to be less consistent.

What insights does the research offer regarding the determinants of systematic and idiosyncratic skewness?

Langlois’s research documents the differences in the determinants of systematic and idiosyncratic skewness. By exploring these distinctions, the study contributes valuable insights into the complexities of skewness premia, enhancing our understanding of the factors influencing stock returns related to skewness.

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