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An Optimal Early Warning System for Currency Crises Under Model Uncertainty

Mamdouh Mohamed, Hany Abdel-Latif, Hany Mohamed

SSRN Electronic Journal

Swansea University Author: Hany Mohamed

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DOI (Published version): 10.2139/ssrn.3428016

Abstract

This paper assesses several early warning (EWS) models of financial crises to propose a model that can predict the incidence of a currency crisis in developing countries. For this purpose, we employ the dynamic model averaging (DMA), and equal weighting (EW) approaches to combine forecast from indiv...

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Published in: SSRN Electronic Journal
ISSN: 1556-5068
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa51894
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first_indexed 2019-09-13T20:30:05Z
last_indexed 2020-06-29T19:04:59Z
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spelling 2020-06-29T15:47:17.0268344 v2 51894 2019-09-13 An Optimal Early Warning System for Currency Crises Under Model Uncertainty 2930976ccf31ef0c71f78f7cb47e2d5d Hany Mohamed Hany Mohamed true false 2019-09-13 This paper assesses several early warning (EWS) models of financial crises to propose a model that can predict the incidence of a currency crisis in developing countries. For this purpose, we employ the dynamic model averaging (DMA), and equal weighting (EW) approaches to combine forecast from individual models allowing for time-varying weights. Taking Egypt as a case study and focusing only on currency crises, our findings show that combined forecast (DMA- and EW-based EWS) to account for uncertainty perform better than other competing models in both in-sample and out-of-sample forecasts. Journal Article SSRN Electronic Journal 1556-5068 Financial Crises, Currency Crises, Early Warning, Uncertainty, Egypt 31 12 2019 2019-12-31 10.2139/ssrn.3428016 COLLEGE NANME COLLEGE CODE Swansea University 2020-06-29T15:47:17.0268344 2019-09-13T17:16:45.6750564 Faculty of Humanities and Social Sciences School of Management - Economics Mamdouh Mohamed 1 Hany Abdel-Latif 2 Hany Mohamed 3 0051894-13092019171937.pdf AbdelsalamAbdel-Latif2019.pdf 2019-09-13T17:19:37.8900000 Output 1763381 application/pdf Author's Original true 2019-09-13T00:00:00.0000000 true eng
title An Optimal Early Warning System for Currency Crises Under Model Uncertainty
spellingShingle An Optimal Early Warning System for Currency Crises Under Model Uncertainty
Hany Mohamed
title_short An Optimal Early Warning System for Currency Crises Under Model Uncertainty
title_full An Optimal Early Warning System for Currency Crises Under Model Uncertainty
title_fullStr An Optimal Early Warning System for Currency Crises Under Model Uncertainty
title_full_unstemmed An Optimal Early Warning System for Currency Crises Under Model Uncertainty
title_sort An Optimal Early Warning System for Currency Crises Under Model Uncertainty
author_id_str_mv 2930976ccf31ef0c71f78f7cb47e2d5d
author_id_fullname_str_mv 2930976ccf31ef0c71f78f7cb47e2d5d_***_Hany Mohamed
author Hany Mohamed
author2 Mamdouh Mohamed
Hany Abdel-Latif
Hany Mohamed
format Journal article
container_title SSRN Electronic Journal
publishDate 2019
institution Swansea University
issn 1556-5068
doi_str_mv 10.2139/ssrn.3428016
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Economics
document_store_str 1
active_str 0
description This paper assesses several early warning (EWS) models of financial crises to propose a model that can predict the incidence of a currency crisis in developing countries. For this purpose, we employ the dynamic model averaging (DMA), and equal weighting (EW) approaches to combine forecast from individual models allowing for time-varying weights. Taking Egypt as a case study and focusing only on currency crises, our findings show that combined forecast (DMA- and EW-based EWS) to account for uncertainty perform better than other competing models in both in-sample and out-of-sample forecasts.
published_date 2019-12-31T04:03:56Z
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score 11.036334

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