Bayesian Switching Volatility Models for Analysing Stock Returns in Ghana

Akurugu Edward, Angbing Irene Dekomwine, Nasiru Suleman () and Abubakari Abdul Ghaniyyu
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Akurugu Edward: Department of Statistics, Faculty of Mathematical Sciences, University for Development Studies, Tamale, Ghana
Angbing Irene Dekomwine: Department of Statistics, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
Nasiru Suleman: Department of Statistics, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
Abubakari Abdul Ghaniyyu: Department of Statistics, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana

Statistics, Politics and Policy, 2022, vol. 13, issue 2, 235-254

Abstract: The goal of this research was to use Bayesian switching volatility models to model the stock returns of the GCB, bank in Ghana. Monthly stock prices of GCB bank for the period of 138 months were used for the study. The two-state Markov-Switching GARCH models were used in the study to determine the best model for modelling and forecasting the stock returns. The Deviance Information Criteria was considered when selecting the best model. Based on the Deviance Information Criteria, E-GARCH variance specification with skewed student-t innovation was shown to be appropriate for modelling the stock returns. The estimates of the best model showed the first regime to exhibit the features of “turbulent market conditions” while the second regime exhibits “tranquil market conditions”. The risk analysis finds the best model to generally perform better in estimating both Value-at-Risk and Expected Shortfall at 1% rather than 5%. The study advises investors to invest in GCB bank because of the high returns connected with the stock and the fact that when “turbulent market conditions” arise, the recovery rate for these stocks is faster.

Keywords: GARCH; Markov switching; conditional distribution; value-at-risk; expected shortfall (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/spp-2022-0002

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