Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/29870
DC Field | Value | Language |
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dc.contributor.author | Petropoulos, Anastasios | - |
dc.contributor.author | Siakoulis, Vasilis | - |
dc.contributor.author | Panousis, Konstantinos P. | - |
dc.contributor.author | Papadoulas, Loukas | - |
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.date.accessioned | 2023-07-14T10:33:36Z | - |
dc.date.available | 2023-07-14T10:33:36Z | - |
dc.date.issued | 2022-11-02 | - |
dc.identifier.citation | Proceedings of the 3rd ACM International Conference on AI in Finance, 2022, 2-4 November, New York, pp. 53 - 61 | en_US |
dc.identifier.isbn | 9781450393768 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/29870 | - |
dc.description.abstract | In the aftermath of the financial crisis, supervisory authorities have considerably altered the mode of operation of financial stress testing. Despite these efforts, significant concerns and extensive criticism have been raised by market participants regarding the considered unrealistic methodological assumptions and simplifications. Current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models. This renders their integration a really challenging task, leading to significant estimation errors. Moreover, advanced statistical techniques that could potentially capture the non-linear nature of adverse shocks are still ignored. This work aims to address these criticisms and shortcomings by proposing a novel approach based on recent advances in Deep Learning towards a principled method for Dynamic Balance Sheet Stress Testing. Experimental results on a newly collected financial/supervisory dataset, provide strong empirical evidence that our paradigm significantly outperforms traditional approaches; thus, it is capable of more accurately and efficiently simulating real world scenarios. | en_US |
dc.language.iso | en | en_US |
dc.rights | © Copyright held by the owner/author(s). | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | Bayesian Model Averaging | en_US |
dc.subject | Bayesian Model Averaging | en_US |
dc.subject | Constant balance sheet | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Dynamic balance sheet | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Neural Networks | en_US |
dc.subject | Stress Testing | en_US |
dc.title | A Deep Learning Approach for Dynamic Balance Sheet Stress Testing | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Mechanical Engineering | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.relation.conference | 3rd ACM International Conference on AI in Finance, ICAIF | en_US |
dc.identifier.doi | 10.1145/3533271.3561656 | en_US |
dc.identifier.scopus | 2-s2.0-85142517288 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85142517288 | - |
cut.common.academicyear | 2022-2023 | en_US |
dc.identifier.spage | 53 | en_US |
dc.identifier.epage | 61 | en_US |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.grantfulltext | open | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
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a deep Learning.pdf | 720.66 kB | Adobe PDF | View/Open |
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