Tick size, spread, trading volume and volatility: application in context of Malaysian stock market / Diana Baharuddin
This study attempts to assess the implementation consequences of small tick size in the context of Malaysian stock market, which take place on 3rd August 2009. Numerous of researchers examine the impact reduction of tick size towards market liquidity, which can be found from around the world, unfort...
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/39478/1/39478.pdf |
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| Summary: | This study attempts to assess the implementation consequences of small tick size in the context of Malaysian stock market, which take place on 3rd August 2009. Numerous of researchers examine the impact reduction of tick size towards market liquidity, which can be found from around the world, unfortunately, in Malaysia, it is hardly to discover researcher or academician endeavour to examine the consequences of using smaller tick size. Moreover, to make this more complex, proxies to market liquidity used to re-estimate the volatility after the implementation of tick size take place. One of the main connection between tick size and liquidity, it is a tool to improve the market liquidity. This study use daily data, started from the implementation of new tick size from 3rd August 2009 until the end of trading day 31st December 2014 by using components of FTSE-BMKLCI. Using Ordinary Least Square method to analyse the result, this study found that, although often-cited researcher mentioned smaller tick size generally lead to increase or improve the liquidity, this result is not universal. Stocks with higher large tick size experience the greatest improvement in liquidity, yet stocks with small tick size facing wider spread and low trading volume, which experienced reduce in liquidity. This indicates that, the improvement of liquidity applies to the stock that actively traded and improves the liquidity. Whereas, for estimation volatility of spread and trading volume, the evidence suggest TGARCH is better in estimating the volatility of spread, whereas for trading volume EGARCH has better fit the data series into model. The findings also suggest that, the volatility of trading volume using EGARCH able to captures the existence of leverage effect. |
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