A Comparative Analysis of Stock Market Volatility

Seyingbo, Oluwagbenga Abayomi (2019) A Comparative Analysis of Stock Market Volatility. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (757kB) | Preview
Preview	PDF (Configuration manual) Download (608kB) | Preview

Abstract

The importance and estimation of stock market volatility cannot be overemphasized, as it helps in risk management, asset allocation, option pricing and portfolio management, and as such, several attempts have been made by various scholars to build forecasting model that can give accurate predictions of stock market volatility and returns. The primary objective of this study is to compare stock market volatility using the developed stock market index, while the secondary objectives are to investigates the presence of volatility clustering, conditional volatility and leptokurtosis distribution in the stock market index and compare the forecasting ability of symmetry and asymmetry GARCH. The datasets used for this comprises of S & P 500, NSADAQ Composites and DOWJONES covering the period from January, 2015 to June 2019. The symmetry and asymmetry GARCH models adopted for this study are GARCH (1,1), EGARCH (1,1) and GJRGARCH (1,1) and the models were evaluated through Information Criterion such as (AIC), (BIC), (SIC) and (HQIC). The findings, the study reveals that S & P 500, NSADAQ, DOWJONES possesses the same attributes such as high returns, high risk, presence of volatility clustering, serial correlation, leptokurtosis distribution and conditional volatility. The findings also revealed that there is ARCH and GARCH effect in each of the models were positively significant and there exists the presence of leverage or asymmetry effect on S & P 500, NSADAQ Composites and DOWJONES. The study concludes that BIC of and GARCH (1,1) model has the smallest values, and as such, GARCH (1,1) gives the best forecasting ability than EGACRH (1,1) and GJRGACRH (1,1) models.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HG Finance > Investment H Social Sciences > HG Finance > Investment > Stock Exchange
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Dan English
Date Deposited:	03 Jun 2020 10:30
Last Modified:	03 Jun 2020 10:30
URI:	https://norma.ncirl.ie/id/eprint/4228

Actions (login required)

View Item