Granger causality analysis in mean patterns to measure the number of lags of the cross-correlation between the standard residuals of returns and trading volume in crisis situation

Subject Areas : Journal of Investment Knowledge

Mohammad Hasan Saleh ¹ , Fazel Mohammadi Nodeh ² , mojtaba maleki choobari ³

1 - Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Assistant Professor, Department of Management, Lahijan Branch, Islamic Azad University, Lahijan, Iran
3 - Department of accounting, Lahijan Branch, Islamic Azad University, Lahijan, Iran

Received: 2023-07-19 Accepted : 2023-07-29 Published : 2024-09-22

Keywords: trading volume, Granger Causality, Stock Returns, Average Causality,

Abstract :

This study investigates the analysis of Granger causality in mean patterns to measure the number of lags of the cross-correlation between the standard-residuals of returns and the volume of transactions during the crisis period. For this purpose, based on daily data from April 2020 to April 2021, structural breakpoints were first determined and then the relationship between the volume of daily transactions and price changes of the Tehran Stock Exchange was investigated using the GARCH-ARMA model. Finally, the causality was investigated in the average between returns and volume of transactions for each sub-period. The results showed when the prices fall sharply during the crisis period, market participants tend to use the volume of past transactions to predict current returns. Also, the results showed when there is an upward price movement in the post-crisis period, it is observed the correlations are significant from lag 2 to 20. These observations suggest causality-in-the-mean between both series occurs asymmetrically after the crisis period. Such asymmetric behavior supports the trader heterogeneity hypothesis from two perspectives, firstly, the degree of significant cross-correlation between the standardized residuals of both series is stronger in the highest interval before the crisis than after the crisis. Second, the time frame for past trading volume to correlate with current returns becomes longer after the crisis. The results of the research can be used to predict stock prices at a time when we are facing the fall of the stock market and the emergence of behavioral phenomena, especially negative market sentiments.

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