Hybrid Model for Stock Market Volatility
Empirical evidence suggests that the traditional GARCH-type models are unable to accurately estimate the volatility of financial markets. To improve on the accuracy of the traditional GARCH-type models, a hybrid model (BSGARCH (1, 1)) that combines the flexibility of B-splines with the GARCH (1, 1)...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2023/6124649 |
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| _version_ | 1832559672285986816 |
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| author | Kofi Agyarko Nana Kena Frempong Eric Neebo Wiah |
| author_facet | Kofi Agyarko Nana Kena Frempong Eric Neebo Wiah |
| author_sort | Kofi Agyarko |
| collection | DOAJ |
| description | Empirical evidence suggests that the traditional GARCH-type models are unable to accurately estimate the volatility of financial markets. To improve on the accuracy of the traditional GARCH-type models, a hybrid model (BSGARCH (1, 1)) that combines the flexibility of B-splines with the GARCH (1, 1) model has been proposed in the study. The lagged residuals from the GARCH (1, 1) model are fitted with a B-spline estimator and added to the results produced from the GARCH (1, 1) model. The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 100 and S&P 500). The outcome was then compared to the outcomes of the GARCH (1, 1), EGARCH (1, 1), GJR-GARCH (1, 1), and APARCH (1, 1) with different error distributions (ED) using the mean absolute percentage error (MAPE), the root mean square error (RMSE), Theil’s inequality coefficient (TIC) and QLIKE. It was concluded that the proposed BSGARCH (1, 1) model outperforms the traditional GARCH-type models that were considered in the study based on the performance metrics, and thus, it can be used for estimating volatility of stock markets. |
| format | Article |
| id | doaj-art-0cc01b405007421ab81767201a59916a |
| institution | Kabale University |
| issn | 1687-9538 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-0cc01b405007421ab81767201a59916a2025-02-03T01:29:28ZengWileyJournal of Probability and Statistics1687-95382023-01-01202310.1155/2023/6124649Hybrid Model for Stock Market VolatilityKofi Agyarko0Nana Kena Frempong1Eric Neebo Wiah2Department of Mathematical SciencesKwame Nkrumah University of Science and TechnologyDepartment of Mathematical SciencesEmpirical evidence suggests that the traditional GARCH-type models are unable to accurately estimate the volatility of financial markets. To improve on the accuracy of the traditional GARCH-type models, a hybrid model (BSGARCH (1, 1)) that combines the flexibility of B-splines with the GARCH (1, 1) model has been proposed in the study. The lagged residuals from the GARCH (1, 1) model are fitted with a B-spline estimator and added to the results produced from the GARCH (1, 1) model. The proposed BSGARCH (1, 1) model was applied to simulated data and two real financial time series data (NASDAQ 100 and S&P 500). The outcome was then compared to the outcomes of the GARCH (1, 1), EGARCH (1, 1), GJR-GARCH (1, 1), and APARCH (1, 1) with different error distributions (ED) using the mean absolute percentage error (MAPE), the root mean square error (RMSE), Theil’s inequality coefficient (TIC) and QLIKE. It was concluded that the proposed BSGARCH (1, 1) model outperforms the traditional GARCH-type models that were considered in the study based on the performance metrics, and thus, it can be used for estimating volatility of stock markets.http://dx.doi.org/10.1155/2023/6124649 |
| spellingShingle | Kofi Agyarko Nana Kena Frempong Eric Neebo Wiah Hybrid Model for Stock Market Volatility Journal of Probability and Statistics |
| title | Hybrid Model for Stock Market Volatility |
| title_full | Hybrid Model for Stock Market Volatility |
| title_fullStr | Hybrid Model for Stock Market Volatility |
| title_full_unstemmed | Hybrid Model for Stock Market Volatility |
| title_short | Hybrid Model for Stock Market Volatility |
| title_sort | hybrid model for stock market volatility |
| url | http://dx.doi.org/10.1155/2023/6124649 |
| work_keys_str_mv | AT kofiagyarko hybridmodelforstockmarketvolatility AT nanakenafrempong hybridmodelforstockmarketvolatility AT ericneebowiah hybridmodelforstockmarketvolatility |