Trading Volume Explains Little of Clustered Volatility

Summary

This study tests whether fluctuations in trading activity account for clustered volatility and heavy tails in stock returns. Using tick-by-tick observations from the New York and London exchanges, it compares price behavior when measured over intervals with fixed transaction counts or fixed total volume. Volatility clustering remains strong under both conditions, and return distributions retain shapes similar to those observed in ordinary time intervals.

The results indicate that trading frequency and total volume explain only a small part of volatility variation and are not the main source of heavy-tailed returns. A cross-sectional analysis also finds that factors beyond transaction frequency and volume dominate volatility’s long memory. The authors examine why their findings differ from earlier studies. The evidence is limited to the exchanges and data analyzed; the account does not identify which other factors cause the remaining volatility patterns.

Key ideas

• Trading frequency explains only a small share of volatility fluctuations in the studied data.
• Volatility clustering persists when price changes are measured over intervals with fixed transaction counts.
• Fixing total traded volume also leaves strong volatility clustering.
• Returns conditioned on frequency or volume retain distributions similar to those seen in real time.
• Factors beyond frequency and volume dominate the long memory of volatility in the cross-sectional analysis.

Tags

# 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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.