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利用交易量预测极端波动率

文章 arXiv papers · 作者: Zeyu Zheng et al.
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总结

本文研究交易量与波动率的相互作用，特别关注两者都处于高位的时期。文章介绍了一种条件分布分析：考察不同交易量水平下的波动率分布，以幂律和指数截断进行建模，并重新缩放，使其归并到同一条曲线上。文章还将局部最大波动率（LMV）定义为给定交易量范围内的最大波动率，并发现LMV随交易量的对数增加而上升。

报告的实证结果表明，交易量有助于估计当日以及近期未来时段的最大波动率。相比单独使用交易量或波动率，结合两者的联合条件概率在预测次日最大波动率方面表现更好。总结未说明资产、样本、具体预测期限、验证设计或预测误差指标，因此无法据此确定这种关系能否推广至不同市场，或支持可部署的交易策略。

核心观点

  • 研究称，以交易量为条件的波动率服从带指数截断的幂律。
  • 按交易量重新缩放条件波动率分布后，它们会归并到同一条曲线上。
  • 局部最大波动率衡量某一交易量范围内观察到的最高波动率。
  • 交易量的对数与局部最大波动率密切相关。
  • 与单独使用交易量或波动率相比，结合两者能更好地预测次日最大波动率。

标签

波动率统计学风险管理
全文
# Predicting market instability: New dynamics between volume and volatility


# Predicting market instability: New dynamics between volume and volatility









Econophysics and econometrics agree that there is a correlation between volume and volatility in a time series. Using empirical data and their distributions, we further investigate this correlation and discover new ways that volatility and volume interact, particularly when the levels of both are high. We find that the distribution of the volume-conditional volatility is well fit by a power-law function with an exponential cutoff. We find that the volume-conditional volatility distribution scales with volume, and collapses these distributions to a single curve. We exploit the characteristics of the volume-volatility scatter plot to find a strong correlation between logarithmic volume and a quantity we define as local maximum volatility (LMV), which indicates the largest volatility observed in a given range of trading volumes. This finding supports our empirical analysis showing that volume is an excellent predictor of the maximum value of volatility for both same-day and near-future time periods. We also use a joint conditional probability that includes both volatility and volume to demonstrate that invoking both allows us to better predict the largest next-day volatility than invoking either one alone.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。