High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model
<p>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 int...
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2025
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| _version_ | 1864513550797504512 |
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| author | Aryan Bhambu (18767731) |
| author2 | Koushik Bera (20884241) Selvaraju Natarajan (20884244) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author2_role | author author author |
| author_facet | Aryan Bhambu (18767731) Koushik Bera (20884241) Selvaraju Natarajan (20884244) Ponnuthurai Nagaratnam Suganthan (11274636) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aryan Bhambu (18767731) Koushik Bera (20884241) Selvaraju Natarajan (20884244) Ponnuthurai Nagaratnam Suganthan (11274636) |
| dc.date.none.fl_str_mv | 2025-03-11T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2025.110397 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/High_frequency_volatility_forecasting_and_risk_assessment_using_neural_networks-based_heteroscedasticity_model/28602935 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Machine learning 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 | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.110397" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.110397</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7dfd1b1cef0f2483fa8ccc15eaedc5e8 |
| identifier_str_mv | 10.1016/j.engappai.2025.110397 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28602935 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity modelAryan Bhambu (18767731)Koushik Bera (20884241)Selvaraju Natarajan (20884244)Ponnuthurai Nagaratnam Suganthan (11274636)Commerce, management, tourism and servicesBanking, finance and investmentInformation and computing sciencesMachine learningVolatility forecastingRisk assessmentGeneralized autoregressive conditional heteroscedasticity modelsHigh frequency dataNeural networkDeep learningFinance<p>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.</p><h2>Other Information</h2> <p> Published in: Engineering Applications of Artificial Intelligence<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.110397" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.110397</a></p>2025-03-11T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2025.110397https://figshare.com/articles/journal_contribution/High_frequency_volatility_forecasting_and_risk_assessment_using_neural_networks-based_heteroscedasticity_model/28602935CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286029352025-03-11T12:00:00Z |
| spellingShingle | High frequency volatility forecasting and risk assessment using neural networks-based heteroscedasticity model Aryan Bhambu (18767731) Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Machine learning 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 | Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Machine learning Volatility forecasting Risk assessment Generalized autoregressive conditional heteroscedasticity models High frequency data Neural network Deep learning Finance |