Abstract

We investigate the risk–return trade-off on the US and European stock markets. We investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market portfolio. We find that the risk–return trade-off is significantly positive at the upper tail (0.9 quantile), where the upper tail is large positive excess returns. The positive trade-off is as expected from asset pricing models. For the lower tail (0.1 quantile), that is for large negative stock returns, the trade-off is significantly negative. Additionally, for the median (0.5 quantile), the risk–return trade-off is insignificant. These results are recovered for the US industry portfolios and for Eurozone stock market portfolios.

Highlights

  • We investigated the risk–return trade-off on the US and European stock markets

  • We investigated the non-linear risk–return trade-off focusing on the tails of the stock returns using quantile regressions

  • For the US stock market portfolio, the risk–return trade-off was significantly positive at the upper tail, while it was significantly negative at the lower tail

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Summary

Introduction

In this paper we investigate the risk–return trade-off on the US and European stock markets. The contribution of this paper is to investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. Adrian et al (2019b) consider the non-linear risk–return trade-off for 11 US industry portfolios and bonds Their measure if risk is the VIX volatility index. We use quantile regressions to analyze how the current stock excess returns depend on the lagged VIX volatility index. This allows us to investigate if the trade-off differs across quantiles, and we look closer at the differences between the lower tail, the median, and the upper tail.

Stock Returns
Risk Measures
Econometric Method
Empirical Analysis
Market Portfolio
Industry Portfolios
European
Skewness and Effects
Subsample Analysis
Conclusions
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