Trading Volume and Volatility in the US Stock Markets
| Author: | Bc. Tomáš Juchelka |
|---|---|
| Year: | 2014 - summer |
| Leaders: | |
| Consultants: | |
| Work type: | Bachelors |
| Language: | English |
| Pages: | 71 |
| Awards and prizes: | B.A. with distinction from the Dean of the Faculty of Social Sciences for an excellent state-final examination performance. |
| Link: | https://is.cuni.cz/webapps/zzp/detail/139330/ |
| Abstract: | This thesis investigates the relationship between trading volume and stock re- turn volatility using GARCH model in the framework of Mixture of Distri- bution Hypothesis. Analysis is carried out for ve well-known stocks selected from the American S&P500 stock index. Our approach was to extend the vari- ance equation of the well known GARCH model on the trading volume which was split into three explanatory variables capturing di erent e ects of volume on volatility. Apart from the relationship itself, we examined the changes of GARCH and ARCH parameters after the inclusion of volume, implicitly testing the Mixture of Distribution Hypothesis. Interesting results and implications for future research were identi ed. Firstly, we highlight the appropriateness of the volume decomposition into expected and unexpected volume, where all the vol- ume parameters turned out to be statistically signi cant. General observation was that the increase of both expected and unexpected trading volume leads to the increase of volatility. On the other hand, negative volume shocks tend to decrease it. Eventhough we performed the analysis with lagged and also contemporaneous volume, we were not able to con rm that the inclusion of volume leads to insigni cance of the ARCH and GARCH parameters, thus not con rming the Mixture of Distribution Hypothesis. However, we found that the volume models perform signi cantly better than the plain GARCH models according to AIC. Considering these ndings, it is possible to conclude that there is positive relationship between the stock return volatility and trading volume. We also found that the volume models perform substantially better in modeling and predicting the future volatility. |
