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MODELING VOLATILITY OF NIGERIA STOCK EXCHANGE USING GARCH MODELS

Domaine:

socioeconomic

Type de record:

paper
Créateur:
TobFem
Éditeur:
Paper Publications
Hôte:avatar
Abstract: Financial and economic variables fluctuate owing to a variety of causes, including economic conditions, market pressures, government policies, global effects, industry-specific factors, and even random events. Addressing these fluctuations requires the development of accurate forecasting models to help market participants and policymakers adapt to the dynamic nature of stock market volatility. This research models the conditional mean and variance of Nigeria Stock Exchange Banking Index (NGX-BANK) by obtaining the ARIMA model that captures the linear dependency in the return and the optimal Symmetric or Asymmetric GARCH model that captures the time-varying volatility. The research obtained the Value at Risk and forecast future volatility. Ten years daily closing price were used to obtain the estimate of the ARIMA-GARCH, ARIMA-IGARCH, ARIMA-EGARCH and ARIMA-TGARCH models. The returns from the daily price were stationary but not normally distributed showing the asymmetric nature of the returns. ARIMA(2,0,1) captures the linear dependencies and temporal patterns present in the returns of the series. It was established that ARIMA(2,0,1)+EGARCH(4,4) was the optimal model that can capture the structured information regarding conditional mean and volatility. There were no indication of heteroscedasticity or autocorrelation in the ARIMA-EGARCH model's residual. Meanwhile, there exist 1% chance that the loss from the asset will exceed 860.07 in 10 days. Keywords: ARIMA, Forecast, GARCH, Heteroscedasticity, Time Series, VaR, Volatility. Title: MODELING VOLATILITY OF NIGERIA STOCK EXCHANGE USING GARCH MODELS Author: Toba Temitope Bamidele, Femi Barnabas Adebola International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS) ISSN 2350-1049 Vol. 11, Issue 2, April 2024 - June 2024 Page No: 45-53 Paper Publications Website: www.paperpublications.org Published Date: 04-June-2024 DOI: doi.org Paper Download Link (Source) paperpublications.org

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doi.orgzenodo.org

Tags

ARIMAForecastGARCHHeteroscedasticitTime SeriesVaRVolatility

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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