Volatility Modelling using Arch and Garch Models (A Case Study of the Nigerian Stock Exchange)

Arum Kingsley.C; Uche Peter .I

doi:https://doi.org/10.14445/22315373/IJMTT-V65I4P511

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 65 | Issue 4 | Year 2019 | Article Id. IJMTT-V65I4P511 | DOI : https://doi.org/10.14445/22315373/IJMTT-V65I4P511

Volatility Modelling using Arch and Garch Models (A Case Study of the Nigerian Stock Exchange)

Arum Kingsley.C, Uche Peter .I

Citation :

Arum Kingsley.C, Uche Peter .I, "Volatility Modelling using Arch and Garch Models (A Case Study of the Nigerian Stock Exchange)," International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no. 4, pp. 58-63, 2019. Crossref, https://doi.org/10.14445/22315373/IJMTT-V65I4P511

Abstract

ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial application. These models are especially useful when the goal of the study is to analyse and forecast volatility. This study investigates the volatility in equity prices of insurance stocks traded on the floor of the Nigerian Stock Exchange. The time series data covers almost five years starting from 4th of March 2011 to 31st of December 2015 excluding weekends and public holidays resulting to approximately 1,106 observations. This study shows that GARCH(0,3), which is the same as ARCH(3) and GARCH(1,1), is the best model that captures the volatility that exist in the insurance stocks through the information criteria of Akaike, Bayesian, Shibata and Hanna Quinn. Also, Value at Risk (VaR) was examined to determine the maximum expected loss in the insurance stocks on daily basis at 95% confidence level. Finally, Potential investors are thereby advised to invest in insurance stocks as they show calm tranquility, though their present stock prices are low but the future remains bright because their market is relatively stable going by the result of the analysis.

Keywords

GARCH, Volatility, Value at Risk, Insurance Sector.

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