Equity Variance and Tail Risk Premia: Factors and Relationships

Summary

This empirical study examines what drives variance, skewness and kurtosis risk premia for long-term equity investors. It uses profit and loss from a class of swaps on variance and higher moments of log returns as estimators, choosing contracts with discretization-invariant aggregation properties. The analysis focuses on the S&P 500 and considers market excess return alongside equity characteristics and momentum.

The reported findings identify momentum as a leading driver of skewness and kurtosis premia, which are strongly negatively correlated. Variance premia respond positively to size and negatively to growth, while their relationship with tail risk premia is comparatively weak, especially at high sampling frequencies. The authors also emphasize careful data construction to avoid artifacts that could distort estimates. These are empirical associations from the stated market and methodology; the summary does not establish causal effects or generalize the results to other markets.

Key ideas

• Swap profit and loss is used to estimate variance and higher-moment risk premia.
• The study analyzes S&P 500 data using contracts designed to aggregate across discretization choices.
• Momentum is a dominant reported driver of skewness and kurtosis premia.
• Skewness and kurtosis premia are strongly negatively correlated in the study.
• Variance premia relate positively to size and negatively to growth, with a relatively low correlation to tail premia.

Tags

# Tail Risk Premia for Long-Term Equity Investors


# Tail Risk Premia for Long-Term Equity Investors









We use the P&L on a particular class of swaps, representing variance and higher moments for log returns, as estimators in our empirical study on the S&P500 that investigates the factors determining variance and higher-moment risk premia. This class is the discretisation invariant sub-class of swaps with Neuberger's aggregating characteristics. Besides the market excess return, momentum is the dominant driver for both skewness and kurtosis risk premia, which exhibit a highly significant negative correlation. By contrast, the variance risk premium responds positively to size and negatively to growth, and the correlation between variance and tail risk premia is relatively low compared with previous research, particularly at high sampling frequencies. These findings extend prior research on determinants of these risk premia. Furthermore, our meticulous data-construction methodology avoids unwanted artefacts which distort results.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.