VOLATILITY AND RISK ANALYSIS OF SELECTED COMPANIES IN  NIGERIAN STOCK EXCHANGE

JOSEPHINE NNEAMAKA  ONYEKA-UBAKA; KINGSLEY CHIMEZIE  OHANMO

JOSEPHINE NNEAMAKA ONYEKA-UBAKA DEPARTMENT OF STATISTICS, UNIVERSITY OF LAGOS, AKOKA, LAGOS STATE, NIGERIA.
KINGSLEY CHIMEZIE OHANMO DEPARTMENT OF STATISTICS, UNIVERSITY OF LAGOS, AKOKA, LAGOS STATE, NIGERIA.

Keywords: Stock returns, Volatility, GARCH Models, Modelling, Forecasting

Abstract

The study introduces GARCH family models in modelling stock returns volatility on investor’s decision-making and risk management in the Nigerian Stock Exchange market. Data (Dangote Cement PLC, Nigerian Flourmill, Guinness PLC, Nestle PLC and Unilever PLC) are sourced from ng.invest.com. The parameters of ARCH and GARCH models are estimated by maximum likelihood estimation method while the Lagrange Multiplier (LM) test is proposed testing heteroskedasticity. The results show that the data obtained within the sample period exhibit non-normality and no presence of autocorrelation in the squared standardized residuals. The FIGARCH model estimates the daily return series of Dangote Cement PLC, Nigerian Flourmill, Nestle PLC and Unilever PLC while the TGARCH model is more suitable for the Guinness PLC within the sampled period based on our diagnostic checks (Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)). These findings are significant as they provide stakeholders with a deeper understanding of the patterns in the series, the leverage effect and make informed decisions on how to manage the associated risks. It is also important to note that the government's intervention in supporting struggling companies through policy creation is crucial, especially during periods of reduced returns and high inflation.

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