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A Logistic Model on Panel Data for Systemic Risk Assessment – Evidence from Advanced and Developing Economies
Keywords
systemic risk, early warning systems, financial crisis, binary variables panel data
Abstract
The present paper proposes a framework for developing a new early warning system (EWS) for identifying systemic banking risk and finding the macroeconomic indicators which turn to be the best indicators in predicting stressful situation on the financial market. The research problem is very much debated in the specialty literature, as the exposure of the financial system is generally derived from deteriorating macroeconomic conditions. We propose a logistic model applied on two panel data sets – advanced and emerging economies. Results are satisfactory, as apart from the GDP Growth or Debt level, as main triggers for financial stress situation, we also find the Output Gap as a significant early warning signal for predicting financial crisis.