Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/208086
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chuliá Soler, Helena | - |
dc.contributor.author | Uribe Gil, Jorge Mario | - |
dc.contributor.author | Khalili, Sabuhi | - |
dc.date.accessioned | 2024-02-26T11:57:40Z | - |
dc.date.available | 2024-02-26T11:57:40Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://hdl.handle.net/2445/208086 | - |
dc.description.abstract | We propose generative artificial intelligence to measure systemic risk in the global markets of sovereign debt and foreign exchange. Through a comparative analysis, we explore three novel models to the economics literature and integrate them with traditional factor models. These models are: Time Variational Autoencoders, Time Generative Adversarial Networks, and Transformer-based Time-series Generative Adversarial Networks. Our empirical results provide evidence in support of the Variational Autoencoder. Results here indicate that both the Credit Default Swaps and foreign exchange markets are susceptible to systemic risk, with a historically high probability of distress observed by the end of 2022, as measured by both the Joint Probability of Distress and the Expected Proportion of Markets in Distress. Our results provide insights for governments in both developed and developing countries, since the realistic counterfactual scenarios generated by the AI, yet to occur in global markets, underscore the potential worst-case scenarios that may unfold if systemic risk materializes. Considering such scenarios is crucial when designing macroprudential policies aimed at preserving financial stability and when measuring the effectiveness of the implemented policies. | ca |
dc.format.extent | 46 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.publisher | Universitat de Barcelona. Facultat d'Economia i Empresa | ca |
dc.relation.isformatof | Reproducció del document publicat a: https://www.ub.edu/irea/working_papers/2024/202402.pdf | - |
dc.relation.ispartof | IREA – Working Papers, 2024, IR24/02 | - |
dc.relation.ispartofseries | [WP E-IR24/02] | ca |
dc.rights | cc-by-nc-nd, (c) Chuliá Soler et al., 2024 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) | - |
dc.subject.classification | Deute | - |
dc.subject.classification | Risc de crèdit | - |
dc.subject.classification | Principis variacionals | - |
dc.subject.other | Debt | - |
dc.subject.other | Credit risk | - |
dc.subject.other | Variational principles | - |
dc.title | Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI | ca |
dc.type | info:eu-repo/semantics/workingPaper | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
IR24-02_Chulia+Khahli+Uribe.pdf | 3 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License