Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment
<p dir="ltr">Accurate volatility forecasting is crucial for the efficient management of <u>financial systems</u>. However, the dynamic nature and significant variability in financial <u>time series</u> data pose substantial challenges to achieving these foreca...
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2025
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| _version_ | 1864513533192962048 |
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| author | Aryan Bhambu (18767731) |
| author2 | Ponnuthurai Nagaratnam Suganthan (11274636) Selvaraju Natarajan (20884244) |
| author2_role | author author |
| author_facet | Aryan Bhambu (18767731) Ponnuthurai Nagaratnam Suganthan (11274636) Selvaraju Natarajan (20884244) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aryan Bhambu (18767731) Ponnuthurai Nagaratnam Suganthan (11274636) Selvaraju Natarajan (20884244) |
| dc.date.none.fl_str_mv | 2025-04-09T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.neucom.2025.130078 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Heteroscedastic_ensemble_deep_random_vector_functional_link_neural_network_with_multiple_output_layers_for_High_Frequency_Volatility_Forecasting_and_Risk_Assessment/30540872 |
| 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 Deep neural networks Multiple output layers Ensemble deep random vector functional link Time series forecasting High frequency Cryptocurrency |
| dc.title.none.fl_str_mv | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Accurate volatility forecasting is crucial for the efficient management of <u>financial systems</u>. However, the dynamic nature and significant variability in financial <u>time series</u> data pose substantial challenges to achieving these forecasts. The paper introduces a novel Heteroscedastic ensemble deep random vector functional link (HedRVFL) network with multiple output layers for high frequency volatility forecasting and risk assessment. The hidden layers of the model are hierarchical and stacked for deep representation learning to extract complex patterns within the data. The neuron pruning strategy is utilized to eliminate noisy information from random features, thereby improving the network’s performance. The forecast is generated by combining the outputs of each layer through an ensemble method. A comparative analysis was conducted against several existing forecasting methods, utilizing error metrics and statistical tests on sixteen high frequency cryptocurrency time-series datasets, demonstrating that the proposed model outperforms others in terms of forecasting accuracy and risk assessment.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurocomputing<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.neucom.2025.130078" target="_blank">https://dx.doi.org/10.1016/j.neucom.2025.130078</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9a36859779e2058292fa28dd87c3b27f |
| identifier_str_mv | 10.1016/j.neucom.2025.130078 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30540872 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk AssessmentAryan Bhambu (18767731)Ponnuthurai Nagaratnam Suganthan (11274636)Selvaraju Natarajan (20884244)Commerce, management, tourism and servicesBanking, finance and investmentInformation and computing sciencesMachine learningVolatility forecastingDeep neural networksMultiple output layersEnsemble deep random vector functional linkTime series forecastingHigh frequencyCryptocurrency<p dir="ltr">Accurate volatility forecasting is crucial for the efficient management of <u>financial systems</u>. However, the dynamic nature and significant variability in financial <u>time series</u> data pose substantial challenges to achieving these forecasts. The paper introduces a novel Heteroscedastic ensemble deep random vector functional link (HedRVFL) network with multiple output layers for high frequency volatility forecasting and risk assessment. The hidden layers of the model are hierarchical and stacked for deep representation learning to extract complex patterns within the data. The neuron pruning strategy is utilized to eliminate noisy information from random features, thereby improving the network’s performance. The forecast is generated by combining the outputs of each layer through an ensemble method. A comparative analysis was conducted against several existing forecasting methods, utilizing error metrics and statistical tests on sixteen high frequency cryptocurrency time-series datasets, demonstrating that the proposed model outperforms others in terms of forecasting accuracy and risk assessment.</p><h2>Other Information</h2><p dir="ltr">Published in: Neurocomputing<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.neucom.2025.130078" target="_blank">https://dx.doi.org/10.1016/j.neucom.2025.130078</a></p>2025-04-09T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.neucom.2025.130078https://figshare.com/articles/journal_contribution/Heteroscedastic_ensemble_deep_random_vector_functional_link_neural_network_with_multiple_output_layers_for_High_Frequency_Volatility_Forecasting_and_Risk_Assessment/30540872CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305408722025-04-09T06:00:00Z |
| spellingShingle | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment Aryan Bhambu (18767731) Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Machine learning Volatility forecasting Deep neural networks Multiple output layers Ensemble deep random vector functional link Time series forecasting High frequency Cryptocurrency |
| status_str | publishedVersion |
| title | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| title_full | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| title_fullStr | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| title_full_unstemmed | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| title_short | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| title_sort | Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment |
| topic | Commerce, management, tourism and services Banking, finance and investment Information and computing sciences Machine learning Volatility forecasting Deep neural networks Multiple output layers Ensemble deep random vector functional link Time series forecasting High frequency Cryptocurrency |