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交易量对波动聚集的解释有限

文章 arXiv papers · 作者: Laszlo Gillemot et al.
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总结

本研究检验交易活动波动是否能解释股票收益的波动聚集和厚尾。研究使用纽约和伦敦交易所逐笔观测数据，比较按固定成交笔数或固定总成交量划分区间时的价格行为。在这两种情况下，波动聚集仍然显著，收益分布也保留了与普通时间区间相似的形状。

结果表明，交易频率和总成交量只能解释一小部分波动率变化，并非厚尾收益的主要来源。横截面分析还发现，交易频率和成交量以外的因素主导着波动率的长记忆。作者探讨了这些发现与早期研究不同的原因。证据仅限于所分析的交易所和数据；文中没有指出哪些其他因素造成了剩余的波动模式。

核心观点

  • 在所研究的数据中，交易频率只能解释波动率变化的一小部分。
  • 按固定成交笔数划分区间来衡量价格变化时，波动聚集仍然存在。
  • 固定总成交量也无法消除显著的波动聚集。
  • 按频率或成交量划分的收益，其分布仍与实时观察到的分布相似。
  • 在横截面分析中，频率和成交量以外的因素主导着波动率的长记忆。

标签

股票统计学市场微观结构
全文
# There's more to volatility than volume


# There's more to volatility than volume









It is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility fluctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyze recent results of Ane and Geman (2000) and Gabaix et al. (2003), and discuss the reasons why their conclusions differ from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume.

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。