Tail Risk Premia for Long-Term Equity Investors

TL;DR

This paper empirically investigates the determinants of variance and higher-moment risk premia for long-term equity investors using swap-based estimators on the S&P 500, revealing key drivers like momentum and size.

Contribution

It introduces a novel empirical approach using discretisation-invariant swaps to accurately estimate and analyze variance and higher-moment risk premia, extending prior research.

Findings

Momentum drives skewness and kurtosis risk premia.

Variance risk premium is positively related to size and negatively to growth.

Low correlation between variance and tail risk premia at high sampling frequencies.

Abstract

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.