Work detail

Trading Volume and Volatility in the US Stock Markets

Author: Bc. Tomáš Juchelka
Year: 2014 - summer
Leaders: PhDr. Boril Šopov, MSc., LL.M.
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.
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.


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