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Trading Activity as an Information Proxy in GARCH Stock Volatility Models

Article arXiv papers · Author: Tetsuya Takaishi et al.
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Summary

The study examines whether daily trading volume and transaction counts help explain stock volatility, using data from the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, it treats both measures as proxies for the rate at which market-relevant information arrives. Their relationship to volatility is assessed with generalized autoregressive conditional heteroscedasticity models, which capture persistence in conditional variance.

The reported finding is that adding volume or transaction counts does not always eliminate GARCH effects. The authors interpret this as evidence that these activity measures do not adequately capture information arrival rates. The result cautions against assuming that heavier trading alone accounts for persistent volatility. The supplied description does not specify the sample period, stocks, model variants, or the frequency of cases in which persistence remains, so the strength and generality of the finding cannot be judged from this summary alone.

Key ideas

  • The study tests trading volume and transaction counts as proxies for information arrival.
  • It uses daily Tokyo Stock Exchange data and GARCH models to assess volatility persistence.
  • Adding either trading measure does not always remove GARCH effects.
  • The reported result suggests that volume and transaction counts are incomplete measures of information flow.
  • The document does not give enough methodological detail to assess the result’s scope.

Tags

EquitiesVolatilityStatisticsMarket microstructure
Full text
# The relationship between trading volumes, number of transactions, and stock volatility in GARCH models


# The relationship between trading volumes, number of transactions, and stock volatility in GARCH models









We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatility. The impact of trading volumes or number of transactions on volatility is measured using the generalized autoregressive conditional heteroscedasticity (GARCH) model. We find that the GARCH effects, that is, persistence of volatility, is not always removed by adding trading volumes or number of transactions, indicating that trading volumes and number of transactions do not adequately represent the rate of information arrivals.

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.