Stock price volatility modelling and the relationship with GDP using Garch model

Chukwuemeka, Anulika Oluchukwu (2020) Stock price volatility modelling and the relationship with GDP using Garch model. Masters thesis, Dublin, National College of Ireland.

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Abstract

The stock market is an outstanding sector of any given economy. In most cases, the stock market is being used to determine the strength of the economy. A growing stock market is indicative of a growing economy while a decreasing stock market is indicative of a contracting economy. This research studies two broad objectives. First, we seek to know the credibility of the GARCH model in terms of handling the randomness in stock price and secondly, we investigated the relationship between GDP and the insurance stock prices. Dataset used were obtained from the Central bank Of Nigeria (CBN) and m.investing.com covering from January 2012 to May 2020. The Exponential GARCH (eGARCH) and GJR GARCH models were used to investigate the distribution and the volatility clustering of the return. The Information criterion (Akaike, Bayesian, etc.) was used to determine the best model between the eGARCH and GJR GARCH model. The linear regression model was used to investigate the linear relationship and its significance between the Nigeria Total GDP and the Insurance stock market index. From the result, we observed that the eGARCH model is the better model for modelling the volatility of the daily returns of the Nigeria Insurances stock market index. Also, the total GDP has a negative and significant relationship on the Nigeria Insurance Stock index. We thus recommend that other researcher should other machine learning model for predicting the insurance stock prices such as ARIMA, ARIMAx and LSTM. Also, the relationship that exists between GDP and insurance stock prices should be studied using the nonlinear regression model.

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
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Dan English
Date Deposited:	29 Jan 2021 16:26
Last Modified:	29 Jan 2021 16:26
URI:	https://norma.ncirl.ie/id/eprint/4567

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