http://ijfr.sciedupress.com International Journal of Financial Research Vol. 12, No. 1; 2021 Published by Sciedu Press 101 ISSN 1923-4023 E-ISSN 1923-4031 An Early Warning Signal (EWS) Model for Predicting Financial Crisis in Emerging African Economies Kehinde Damilola Ilesanmi 1 & Devi Datt Tewari 1 1 Department of Economics, University of Zululand, KwaDlangezwa, South Africa Correspondence: Kehinde Damilola Ilesanmi, Department of Economics, University of Zululand, Private Bag X1001, KwaDlangezwa, 3886, South Africa. Tel: 27-834-527-999. Received: May 27, 2020 Accepted: November 10, 2020 Online Published: December 24, 2020 doi:10.5430/ijfr.v12n1p101 URL: https://doi.org/10.5430/ijfr.v12n1p101 Abstract The devastating effects of the global financial crisis (GFC) have led to a renewed, global interest in the development of an early warning signal (EWS) model. The purpose of the EWS model is to alert policymakers and other stakeholders to the possibility of the occurrence of a crisis. This study estimates a EWS model for predicting the financial crisis in four emerging African economies using a multinomial logit model and a data set covering the period of January 1980 to December 2017. The result of the study suggests that emerging African economies are more likely to face financial crisis as debts continue to rise without a corresponding capacity to withstand capital flow reversal as well as excessive foreign exchange risk due to currency exposure. The result further indicates that rising debt exposure raises the likelihood of the economies remaining in a state of crisis. This result confirms the significance of a financial stability framework that addresses the issues confronting Africa’s emerging economies such as rising debt profile, liquidity and currency risk exposure. Keywords: Early Warning Signal (EWS), financial crisis, logit, multinomial 1. Introduction The 2007/08 global financial crisis (GFC) which emanated from the United States and spread to other developed and emerging countries resulted in large-scale losses in most economies around the globe (Cunningham and Friedrich, 2016; Ilesanmi and Tewari, 2019a). The devastating effects of the GFC have led to a renewed, global interest in the development of an early warning signal (EWS) model with the purpose of alerting policymakers and other stakeholders to the possibility of the occurrence of a crisis (Ilesanmi and Tewari, 2019b). Determining the state of the financial system is very important for the design of appropriate policy such as countercyclical capital buffers which can assist in reducing losses accompanying the financial crisis (Drehmann and Juselius, 2014; Louzis and Vouldis, 2012). In order to predict systemic risk in the financial system, an EWS model built by Bussiere and Fratzscher (2006) was adopted to afford policymakers a sufficient period to avert or mitigate the impact of the potential financial crisis (Louzis and Vouldis, 2012; Oet, Bianco, Gramlich, and Ong, 2013). The EWS model is used primarily to predict the possibility of the occurrence of a crisis though it might not predict the exact time of the crisis (Caggiano, Calice, and Leonida, 2014). For the EWS, the multinomial logit model was adopted. This method is commonly used in the literature compared to the binomial logit model which is usually subject to post-crisis bias. This is because the binomial logit model does not differentiate the three crisis phases: tranquil period, crisis period and post-crisis period. The multinomial logit model was used by Bussiere and Fratzscher (2006) within a currency crisis framework, while Oet et al. (2013) and Caggiano et al., (2014) applied it within a banking crisis framework. In these studies, the model was used to predict the probability of the occurrence of a crisis (which takes the value of 1 for the first crisis year, 2 for the other crisis years (Note 1) and 0 for non-crisis years), as a function of a vector of several independent variables. Despite growing literature on the development of EWS models, emerging African economies have not received any specific attention in that regard. Most of the previous studies have been subject to survey-based qualitative analysis (Daumont, Le Gall, and Leroux, 2004; Caggiano et al., 2014) and cross-country panels, pooled together with other emerging economies or advanced economies (Davis and Karim, 2008; Drehmann and Juselius, 2014; Oet et al., 2013). According to Van den Berg, Candelon, and Urbain, (2008), building an EWS model for economies with homogenous characteristics performs better than when they are pooled together with other countries outside the region. While most previous studies have focused on developed countries which were the most severely affected by the crisis (Babecky,