Modelling Systemic Risk in Morocco’s Banking System

Ayoub Kyoud (), Cherif El Msiyah and Jaouad Madkour
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Ayoub Kyoud: National School of Commerce and Management, Ibn Tofaïl University, Kenitra 14000, Morocco
Cherif El Msiyah: National School of Commerce and Management, Moulay Ismail University, Meknes 50000, Morocco
Jaouad Madkour: Faculty of Economics and Management, Abdelmalek Essaâdi University, Tetouan 90000, Morocco

IJFS, 2023, vol. 11, issue 2, 1-16

Abstract: The Moroccan banking system suffered a significant impact due to the extreme market conditions caused by the COVID-19 outbreak, which led to an increase in non-performance loans. This, in turn, reduced the value of banks’ assets and their ability to meet their obligations, implicitly raising systemic risk. In such circumstances, the collapse of one financial institution could cause a series of bankruptcies and endanger the overall state of the economy. Given the limited attention devoted to the analysis of systemic risk in the Moroccan banking system, this paper aimed to fill this gap by analyzing the Moroccan banks’ systemic risk exposure and assessing their stability during the COVID-19 crisis, using Quantile Regression Neural Network (QRNN) optimized by Adam algorithm to calibrate the Conditional Value at Risk (CoVaR). This study revealed a significant increase in systemic risk during the pandemic crisis and highlights the suitability of more complex QRNN in assessing systemic risk. The findings emphasize the need for regulators to pay close attention to banks’ risk exposures when implementing measures to mitigate systemic risk, such as increasing banks’ capital requirements or increasing the amount of high-quality liquid assets.

Keywords: systemic risk; artificial neural networks; Moroccan banks (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
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