High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model
High frequency volatility forecasting is essential for timely risk management and informed decision-making in dynamic financial markets. However, accurate forecasting is challenging due to the rapid nature of market movements and the complexity of underlying economic factors. This paper introduces a...
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2025
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| Online Access: | http://dx.doi.org/10.1016/j.engappai.2025.110397 https://www.sciencedirect.com/science/article/pii/S0952197625003975 http://hdl.handle.net/10576/64848 |
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| _version_ | 1857415087575269376 |
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| author | Aryan, Bhambu |
| author2 | Bera, Koushik Natarajan, Selvaraju Suganthan, Ponnuthurai Nagaratnam |
| author2_role | author author author |
| author_facet | Aryan, Bhambu Bera, Koushik Natarajan, Selvaraju Suganthan, Ponnuthurai Nagaratnam |
| author_role | author |
| dc.creator.none.fl_str_mv | Aryan, Bhambu Bera, Koushik Natarajan, Selvaraju Suganthan, Ponnuthurai Nagaratnam |
| dc.date.none.fl_str_mv | 2025-05-11T11:54:06Z 2025-03-11 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.engappai.2025.110397 Nagaratnam, S. (2025). High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model. 0952-1976 https://www.sciencedirect.com/science/article/pii/S0952197625003975 http://hdl.handle.net/10576/64848 149 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Volatility forecasting Risk assessment Generalized autoregressive conditional heteroscedasticity models High frequency data Neural network Deep learning Finance |
| dc.title.none.fl_str_mv | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | High frequency volatility forecasting is essential for timely risk management and informed decision-making in dynamic financial markets. However, accurate forecasting is challenging due to the rapid nature of market movements and the complexity of underlying economic factors. This paper introduces a novel architecture combining Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Multi-layer Perceptron (MLP)-based models for enhanced volatility forecasting and risk assessment, where input variables are processed through GARCH-type models for volatility forecasting. The proposed GARCH-based MLP-Mixer (GaMM) model incorporates the stacking of multi-layer perceptrons, enabling deep representation learning, facilitating the extraction of temporal and feature information through operations along both time and feature dimensions, and addressing the complexity of high-frequency time-series data. The proposed model is evaluated on three high frequency financial times series datasets over three different years. The computational results demonstrate the proposed model’s superior performance over sixteen forecasting methods in three error metrics, Value-at-risk, and statistical tests for high frequency volatility forecasting and risk assessment tasks. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_1a72b5afd6aea53ca343ff4267ad3bac |
| identifier_str_mv | Nagaratnam, S. (2025). High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model. 0952-1976 149 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/64848 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity modelAryan, BhambuBera, KoushikNatarajan, SelvarajuSuganthan, Ponnuthurai NagaratnamVolatility forecastingRisk assessmentGeneralized autoregressive conditional heteroscedasticity modelsHigh frequency dataNeural networkDeep learningFinanceHigh frequency volatility forecasting is essential for timely risk management and informed decision-making in dynamic financial markets. However, accurate forecasting is challenging due to the rapid nature of market movements and the complexity of underlying economic factors. This paper introduces a novel architecture combining Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Multi-layer Perceptron (MLP)-based models for enhanced volatility forecasting and risk assessment, where input variables are processed through GARCH-type models for volatility forecasting. The proposed GARCH-based MLP-Mixer (GaMM) model incorporates the stacking of multi-layer perceptrons, enabling deep representation learning, facilitating the extraction of temporal and feature information through operations along both time and feature dimensions, and addressing the complexity of high-frequency time-series data. The proposed model is evaluated on three high frequency financial times series datasets over three different years. The computational results demonstrate the proposed model’s superior performance over sixteen forecasting methods in three error metrics, Value-at-risk, and statistical tests for high frequency volatility forecasting and risk assessment tasks.Open Access funding provided by the Qatar National Library. The authors also acknowledge the financial support provided by the Science and Engineering Research Board (SERB), Government of India (Grant No. MTR/2021/000425) and the supercomputing resources from the IIT Guwahati for conducting the research work presented in this paper.Elsevier2025-05-11T11:54:06Z2025-03-11Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.engappai.2025.110397Nagaratnam, S. (2025). High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model.0952-1976https://www.sciencedirect.com/science/article/pii/S0952197625003975http://hdl.handle.net/10576/64848149enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/648482025-05-11T19:07:42Z |
| spellingShingle | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model Aryan, Bhambu Volatility forecasting Risk assessment Generalized autoregressive conditional heteroscedasticity models High frequency data Neural network Deep learning Finance |
| status_str | publishedVersion |
| title | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| title_full | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| title_fullStr | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| title_full_unstemmed | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| title_short | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| title_sort | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model |
| topic | Volatility forecasting Risk assessment Generalized autoregressive conditional heteroscedasticity models High frequency data Neural network Deep learning Finance |
| url | http://dx.doi.org/10.1016/j.engappai.2025.110397 https://www.sciencedirect.com/science/article/pii/S0952197625003975 http://hdl.handle.net/10576/64848 |