Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models
This study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as th...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/13/1/3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587566382055424 |
|---|---|
| author | Osei K. Tweneboah Kwesi A. Ohene-Obeng Maria C. Mariani |
| author_facet | Osei K. Tweneboah Kwesi A. Ohene-Obeng Maria C. Mariani |
| author_sort | Osei K. Tweneboah |
| collection | DOAJ |
| description | This study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as the Hurst exponent and <i>R</i>/<i>S</i> analysis to uncover its fractal properties and complex dynamics. The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s <i>t</i> errors, SV with leverage effects, and a hybrid model combining Student’s <i>t</i> errors with leverage. Each model offers a unique perspective on forecasting the behavior of the GSE-CI, with the SV model incorporating Student’s <i>t</i> errors emerging as the most effective, as evidenced by the lowest Root Mean Square Error (RMSE) in our comparative evaluation. The integration of these models highlights their robustness in capturing the intricate volatility patterns of the GSE-CI, making a compelling case for their applicability to similar financial markets in other emerging economies. This research also paves the way for future investigations into other market indices and assets within and beyond the borders of emerging markets. |
| format | Article |
| id | doaj-art-4bc81bf900d24f669ca81e1e0ac96e33 |
| institution | Kabale University |
| issn | 2227-9091 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj-art-4bc81bf900d24f669ca81e1e0ac96e332025-01-24T13:48:18ZengMDPI AGRisks2227-90912024-12-01131310.3390/risks13010003Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility ModelsOsei K. Tweneboah0Kwesi A. Ohene-Obeng1Maria C. Mariani2Ramapo Data Science Program, Ramapo College of New Jersey, Mahwah, NJ 07430, USADepartment of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USADepartment of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USAThis study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as the Hurst exponent and <i>R</i>/<i>S</i> analysis to uncover its fractal properties and complex dynamics. The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s <i>t</i> errors, SV with leverage effects, and a hybrid model combining Student’s <i>t</i> errors with leverage. Each model offers a unique perspective on forecasting the behavior of the GSE-CI, with the SV model incorporating Student’s <i>t</i> errors emerging as the most effective, as evidenced by the lowest Root Mean Square Error (RMSE) in our comparative evaluation. The integration of these models highlights their robustness in capturing the intricate volatility patterns of the GSE-CI, making a compelling case for their applicability to similar financial markets in other emerging economies. This research also paves the way for future investigations into other market indices and assets within and beyond the borders of emerging markets.https://www.mdpi.com/2227-9091/13/1/3Stochastic Volatility modelsfinancial time seriesGhana Stock Exchange Composite IndexHurst exponent<i>R</i>/<i>S</i> analysis |
| spellingShingle | Osei K. Tweneboah Kwesi A. Ohene-Obeng Maria C. Mariani Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models Risks Stochastic Volatility models financial time series Ghana Stock Exchange Composite Index Hurst exponent <i>R</i>/<i>S</i> analysis |
| title | Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models |
| title_full | Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models |
| title_fullStr | Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models |
| title_full_unstemmed | Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models |
| title_short | Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models |
| title_sort | characterization and prediction of the ghana stock exchange composite index utilizing bayesian stochastic volatility models |
| topic | Stochastic Volatility models financial time series Ghana Stock Exchange Composite Index Hurst exponent <i>R</i>/<i>S</i> analysis |
| url | https://www.mdpi.com/2227-9091/13/1/3 |
| work_keys_str_mv | AT oseiktweneboah characterizationandpredictionoftheghanastockexchangecompositeindexutilizingbayesianstochasticvolatilitymodels AT kwesiaoheneobeng characterizationandpredictionoftheghanastockexchangecompositeindexutilizingbayesianstochasticvolatilitymodels AT mariacmariani characterizationandpredictionoftheghanastockexchangecompositeindexutilizingbayesianstochasticvolatilitymodels |