The Effect of Macroeconomic Shocks on the Liquidity Risk of the Banking system: MS-VAR Approach

Document Type : Research Paper


1 Assistant Prof, Department of Financial and Banking, Faculty of Management and Accounting, Allameh Tabataba'i University Tehran, Iran.

2 MSc, Department of Financial and Banking, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.



Objective: Considering the importance and special position of the banking system in Iran's economy, it should not be overlooked that the fluctuations of macroeconomic variables affect the performance of the banking system. The biggest challenge of the banking industry is identifying and managing the risks in the banking system and reacting to economic shocks. Liquidity risk is one of the most important risks in the Iranian banking system, which exposes it to serious crises. The purpose of this research is to investigate macroeconomic shocks on liquidity risk indicators in Iranian banks admitted to the Tehran Stock Exchange (TSE) and the Iranian Over-the-Counter (OTC).
Methods: To evaluate the impact of macroeconomic shocks on the considered indicators related to liquidity risk, extensive systemic shocks were considered. In the first stage, the relationship between macroeconomic variables was estimated using the MS-VAR model to predict the trend during a specific period. In the second stage, the relationship between liquidity risk indicators (the ratio of liquid assets to total assets and the ratio of the debts to the Central Bank of Iran to total debt) with macroeconomic variables was estimated, using the Panel Data model. Finally, the effect of the probable but exceptional shocks of the macroeconomic variables, extracted in the first stage, on the considered indicators related to liquidity risk was investigated. The model presented in this research used information on the Iranian banking system from 2009 to 2019 to analyze the intended effects.
Results: The results showed that the MSIAH(2)-VAR(1) model was selected as the optimal model. The stability of both regimes has been confirmed. The probability of being in regime I stood at 0/51, and regime II was 0/49. The shocks to macroeconomic variables, the shock to GDP growth in regimes II and I, the shock to inflation in regimes I and II, and the shock to exchange rate growth in regimes II and I, respectively, had the greatest impact on the first index of liquidity risk. For the second index of liquidity risk, respectively, the shock to the GDP growth in the first and second regimes, the shock to inflation in the second regime, the shock to the GDP growth in the second regime, and the shock to the exchange rate growth in the II and I had the most significant impact.
Conclusion: Estimating the ratio of cash assets to total assets, as the first indicator, and the ratio of debt to the Central Bank to total liabilities, as the second indicator, with the shock to macroeconomic variables indicated that liquidity risk indicators were strongly affected in both regimes under the crises. According to the results obtained from both of the indicators, among the considered macroeconomic variables, the shock to GDP growth in regime I had the greatest impact on both liquidity risk indicators.


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