Bayesian Switching Volatility Models for Analysing Stock Returns in Ghana
Bibliographic Data
| ID | 13036678 |
|---|---|
| Authors | Edward Akurugu (0009-0000-4550-0541, University for Development Studies), Irene Dekomwine Angbing (0000-0001-9924-7086, C.K. Tedam University of Technology and Applied Sciences), Suleman Nasiru (0000-0001-6652-4251, C.K. Tedam University of Technology and Applied Sciences, corresponding author), Abdul Ghaniyyu Abubakari (0000-0003-0423-8465, C.K. Tedam University of Technology and Applied Sciences) |
| Year | 2022 |
| Volume | 13 |
| Issue | 2 |
| Pages | 235-254 |
| Publication date | 2022-05-30 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Statistics Politics and Policy (JOURNAL) |
| Journal identifiers | ISSN: 2151-7509 • E-ISSN: 2194-6299 |
| Publisher | De Gruyter (PUBLISHER • DE) |
| DOI | 10.1515/spp-2022-0002 |
| OpenAlex | W4281787143 |
| Language | EN |
| References cited | 29 |
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
Autoregressive conditional heteroskedasticity · Bayesian inference · Bayesian probability · Deviance (statistics · Deviance information criterion · Econometrics · Economics · Financial economics · Geography · Markov chain · Markov chain Monte Carlo · Realized variance · Statistics · Stock (firearms · Stock market · Volatility (finance · Engineering · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact
Conditional Heteroskedasticity in Asset Returns
Answering the Skeptics
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Generalized autoregressive conditional heteroskedasticity
Testing the null hypothesis of stationarity against the alternative of a unit root
Distribution of the Estimators for Autoregressive Time Series with a Unit Root
Bayesian Measures of Model Complexity and Fit
Deviance Information Criterion for Comparing Stochastic Volatility Models
Persistence in Variance, Structural Change, and the GARCH Model
Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects
| Citation velocity | historical |
|---|---|
| Highly cited | No |