杠杆效应与均值回归如何影响收益分布
文章 arXiv papers · 作者: Dangxing Chen
总结
本文探讨杠杆效应虽被广泛观察到,却为何可能对某些资产的收益分布几乎没有明显影响。杠杆效应描述资产收益与波动率变化之间通常为负的关系。作者提出的解释是该效应与均值回归存在交互:杠杆效应较强且均值回归较弱时,它对收益分布的影响最大;而即使杠杆效应较强,强烈的均值回归也可能减弱其影响。作者还提出了一种间接测量这种交互的方法。
实证应用报告称,标普 500 数据中的交互效应较弱,这与杠杆效应对其收益分布影响较小相符。交互效应也与公司规模有关:较小公司的交互效应往往更强,较大公司则往往较弱。本文没有给出数值估计或测量流程细节,其发现也无法证明这些关系适用于所有资产或样本。
核心观点
- 杠杆效应是收益与波动率变化之间通常为负的相关关系。
- 杠杆效应对收益分布的影响取决于它与均值回归的相互作用。
- 据报告,杠杆效应较强且均值回归较弱时,二者的交互最强。
- 作者将所提间接测量方法应用于实证数据。
- 标普 500 数据显示交互效应较弱,而较小公司的交互效应往往强于较大公司。
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# Does the leverage effect affect the return distribution? # Does the leverage effect affect the return distribution? The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointed out a puzzle: the return distributions of many assets do not appear to be affected by the leverage effect. We analyze the determinants of the return distribution and find that the impact of the leverage effect comes primarily from an interaction between the leverage effect and the mean-reversion effect. When the leverage effect is large and the mean-reversion effect is small, then the interaction exerts a strong effect on the return distribution. However, if the mean-reversion effect is large, even a large leverage effect has little effect on the return distribution. To better understand the impact of the interaction effect, we propose an indirect method to measure it. We apply our methodology to empirical data and find that the S&P 500 data exhibits a weak interaction effect, and consequently its returns distribution is little impacted by the leverage effect. Furthermore, the interaction effect is closely related to the size factor: small firms tend to have a strong interaction effect and large firms tend to have a weak interaction effect.
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