Mamdouh Abdelmoula M. Abdelsalam , Hany Abdel-Latif
{"title":"An optimal early warning system for currency crises under model uncertainty","authors":"Mamdouh Abdelmoula M. Abdelsalam , Hany Abdel-Latif","doi":"10.1016/j.cbrev.2020.03.002","DOIUrl":null,"url":null,"abstract":"<div><p>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 equal weighting (EW) and dynamic model averaging (DMA) 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 (EW- and DMA-based EWS), to account for uncertainty, perform better than other competing models in both in-sample and out-of-sample forecasts.</p></div>","PeriodicalId":43998,"journal":{"name":"Central Bank Review","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cbrev.2020.03.002","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Central Bank Review","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1303070120300123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
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 equal weighting (EW) and dynamic model averaging (DMA) 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 (EW- and DMA-based EWS), to account for uncertainty, perform better than other competing models in both in-sample and out-of-sample forecasts.