Ebook Returns, Trading Volume And Volatility In The Stock Market Of Mauritius
An understanding of the relationship between returns, volatility and volume is crucial for the appreciation of the microstructure of financial markets. It helps to comprehend the role of information in the price formation of stocks, with volume and volatility providing measures of significance of information reflected in the market.
It might eventually lead to better volatility forecasting, further exploration of this relation is worth pursuing. Granger and Poon (2003) comment ‘the volume-volatility research may lead to a new and better way for modeling returns distributions’. In the light of these arguments, it will be instructive to examine the returns, volatility and trading volume relationships.
Market folklore suggests that trading volume is positively related to stock return volatility. In the literature, the informational content of volume has been analyzed through two main informational hypotheses namely the Mixture of Distributions Hypothesis and the Sequential Arrival of Information Hypothesis. Researchers found relationships running from returns to volume and vice-versa in stock markets.
Irrespective of the approach, the consensus in the volume volatility literature to date is that a strong link exists between contemporaneous trading volume and conditional volatility (Girard and Biswas 2007). This goes against the weak form of the Efficient Market Hypothesis of Fama (1965) which says that abnormal returns cannot be made by using past information, which trading volume forms part of. However, support is found among chartists and technical analysts who rely on patterns of prices, returns and trading volume to devise their trading strategies.
This paper analyse the relationship between return, volatility and trading volume using data from the Stock Market of Mauritius (SEM). It can be argued that an increase in price induce investors to trade more, thereby increasing trading volume. Conversely, we can hypothesise that an increase in trading volume can lead to an increase in price. Such a study has never been carried out on the Stock Exchange of Mauritius (hereafter SEM).
The analysis has been done using 36 listed companies on the official market, industry-wise and as the market as a whole. To achieve our objectives several versions of the Autoregressive Conditional Heteroscedasticity (hereafter ARCH) family is adopted namely the simple ARCH model, GARCH and GJR-TGARCH. Analysis has also been done to include a risk component in the models by using ARCH-in-mean models. Further, two conditional mean equations, the random walk and the AR (1) have been employed.
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