Modelling volatility in stock exchange data : a case study of three Johannesburg Stock Exchange (JSE) companies.
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Date
2015
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Abstract
For investors and policy makers such as governments, the uncertainty of returns on investments
is a major problem. The aim of this paper is to study volatility models for financial
data for both univariate and multivariate case. The data to be used is monthly and daily
asset returns of three different companies. For the univariate case, the main focus is on
GARCH models and their subsequent derivatives. ARCH and GARCH models of different
orders are fit. For the monthly data, the GARCH(1,1)outperformed the ARCH and
higher order GARCH models. For the daily data, the GARCH(1,1) preceded by an appropriate
AR model was the best fit. For the Multivariate volatility models, models such
as the DCC-GARCH, EMWA and Go-GARCH were used. All three gave similar results.
Various distributional assumptions such as the normal and Student t distributions were
assumed for the innovations. Student t and Skewed Student t distributions were more
effective because of their ability to capture fat tails of the distributions. Fundamental
finance terms and concepts are also discussed.
Description
Master of Science in Statistics.
Keywords
Johannesburg Stock Exchange., Trading rooms (Finance) -- South Africa -- Johannesburg -- Mathematical models., Stock exchanges -- Ratings and rankings -- South Africa -- Johannesburg., Theses -- Statistics., GARCH models., ARCH models.