A Bayesian approach for more reliable tail risk forecasts
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129565751. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.
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| ID Code: | 239119 | ||||||
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| Item Type: | Contribution to Journal (Journal Article) | ||||||
| Refereed: | Yes | ||||||
| ORCID iD: |
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| Additional Information: | Acknowledgement: We gratefully acknowledge the helpful feedback and valuable suggestions of the three anonymous reviewers and the journal editor. DL is supported by a scholarship from the QUT Centre for Data Science and a supervisor’s top-up scholarship from the school of Mathematical Sciences of QUT . Computational resources used in this work were provided by QUT’s High Performance Computing and Research Support Group (HPC). | ||||||
| Measurements or Duration: | 22 pages | ||||||
| Keywords: | CAViaR, Expected shortfall, Sequential Monte Carlo, Systemic risk, Uncertainty quantification, Value-at-risk | ||||||
| DOI: | 10.1016/j.jfs.2022.101098 | ||||||
| ISSN: | 1572-3089 | ||||||
| Pure ID: | 129565751 | ||||||
| Divisions: | Current > Research Centres > Centre for Data Science Current > QUT Faculties and Divisions > Faculty of Business & Law Current > Schools > School of Economics & Finance Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Mathematical Sciences |
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| Funding Information: | We gratefully acknowledge the helpful feedback and valuable suggestions of the three anonymous reviewers and the journal editor. DL is supported by a scholarship from the QUT Centre for Data Science and a supervisor’s top-up scholarship from the school of Mathematical Sciences of QUT . Computational resources used in this work were provided by QUT’s High Performance Computing and Research Support Group (HPC). We gratefully acknowledge the helpful feedback and valuable suggestions of the three anonymous reviewers and the journal editor. DL is supported by a scholarship from the QUT Centre for Data Science and a supervisor's top-up scholarship from the school of Mathematical Sciences of QUT . Computational resources used in this work were provided by QUT's High Performance Computing and Research Support Group (HPC). | ||||||
| Copyright Owner: | 2022 Elsevier B.V. | ||||||
| Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
| Deposited On: | 13 Apr 2023 00:21 | ||||||
| Last Modified: | 03 Mar 2024 06:50 |
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