Cross-Section of Option Returns and the Volatility Risk Premium

Simon Fritzsch, Felix Irresberger, Gregor N. F. Weiß
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引用次数: 1

Abstract

This paper presents a robust new finding that delta-hedged equity option returns include a volatility risk premium. To separate volatility risk premia from confounding effects, we estimate conditional quantile curves of implied volatilities using machine learning. We find that a zero-cost trading strategy that is long (short) in the portfolio with low (high) implied volatility -- conditional on the options' moneyness and realized volatility -- produces an economically and statistically significant average monthly return. Using conditional quantile curves not only helps in distinguishing volatility risk premia from other effects, most notably realized volatility, it also leads to returns that are higher than those reported in previous work on similar volatility strategies.
期权收益与波动率风险溢价的横截面
本文提出了一个稳健的新发现,即delta对冲的股票期权收益包含波动性风险溢价。为了从混杂效应中分离波动风险溢价,我们使用机器学习估计隐含波动率的条件分位数曲线。我们发现,在低(高)隐含波动率(以期权的货币性和已实现波动率为条件)的投资组合中,做多(做空)零成本交易策略产生了经济上和统计上显著的平均月回报。使用条件分位数曲线不仅有助于将波动风险溢价与其他效应区分开来,最显著的是已实现波动率,而且还会导致比以前在类似波动率策略上的研究报告更高的回报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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