SAFE: An early warning system for systemic banking risk

Abstract

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk, incorporating the structural characteristics of the fi nancial system and a feedback amplifi cation mechanism. The models explain fi nancial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the fi nancial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifi cations that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifi cations for verifi cation and adjustment of supervisory actions. This paper highlights the application of these models to stress testing, scenario analysis, and policy.

An Early Warning Signal (EWS) Model for Predicting Financial Crisis in Emerging African Economies

“…The results further show that cases of crisis in low-income economies are usually linked to low economic growth, which resultantly puts enormous pressure on liquidity in the banking sector. Oet et al, (2013) developed a hybrid class of model based on the existing microprudential and macroprudential EWS models for systemic risk that incorporates the structural characteristics of the financial system to explain financial stress. Based on the findings of the study, a model known as the Systemic Assessment of Financial Environment (SAFE) EWS was developed to monitor the build-up of macroeconomic stress in the financial market.…”

“…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).…”

“…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.…”

Using Granger Causality to Determine Interconnectedness in Unlisted Banking Markets

“…The significance of causality is classified in 3 intervals, namely: p<0.05 -strong causality; 0.5<p<0.1 -average causality; 0.1<p<0.15 -weak causality. Significance levels have also been ranked in (Oet , 2013) However, they use significance level up to 20%.…”