Liquidity Risk

Banks as one of the most important and crucial economic sectors in each country play a significant role in economic growth and development and they face various risks one of which is liquidity risk. Managing liquidity risk is of great importance and identifying its effective factors is more vital. The present study aims to pre-sent a dynamic model to manage liquidity risk. System dynamics is used to find a risk making structure and present the most effective solution to manage it. In this method, by providing a mathematical model, simu-lating the results of various scenarios is possible. The results of implement-ing four scenarios on the model were simulated and analyzed. The results revealed that reducing legal deposits and nonperforming loans and increasing attraction of deposits is influential in banks liquidity risk.

In financial markets, the main component of risk management is liquidity risk. Asset and Liability Management (ALM) strategy is concerned with managing all risks. Asset and liability management seeks to manage liquidity risk, which refers to both the liquidity of markets and which assets can be translated into cash. The liquidity is importantly affected by the management of banks’ balance sheets. This paper contributes to the discussion by focusing on liquidity and asset and liability management by providing a theoretical framework to examine how the ALM could be reduced the liquidity risk in banking. We investigate the effect of ALM indicators on liquidity risk. We measure liquidity risk, and ALM with indicators approach, using financial statement in 2006-2018 and panel data approach. The results indicate that if asset and liability management improves, liquidity risk decrease and if the ratio of capital adequacy increases, the bank can better cover liquidity risk, and so increasing in capital adequacy will reduce liquidity risk. Deposit per shareholders increases the liquidity risk of banks. Interest rate increases liquidity risk. When profitability increases, liquidity risk will increase. The relation between liquidity risk and profitability is positive.

The present study suggests a model for predicting liquidity gap, based on source and cost of funds approach concerning the daily time series data (25 March 2009 to 19 March 2018), in order to control and manage the liquidity risk. Using the family of autoregressive conditional heteroscedasticity models, the behavior of bank liquidity gap is modeled and predicted. The results show that the APGARCH with the Johnson-SU distribution is the most suitable model for explaining the liquidity gap behavior. Based on the rolling window method the more accurate model has been selected to be the APGARCH model with T-Student distribution which provides the least error in forecasting liquidity gap.

This study evaluates the economic capital levels for Iranian banks and analyzes their behavior under various macroeconomic shocks by developing a Dynamic Stochastic General Equilibrium (DSGE) model that incorporates banking sector heterogeneity and macroeconomic shock transmission mechanisms. A comprehensive DSGE framework was constructed and calibrated using Iranian macroeconomic and banking data. The model implementation utilized Dynare software to analyze impulse response functions across technology, liquidity, and monetary policy shock scenarios, with particular emphasis on capital adequacy dynamics, bank bankruptcy and exit possibilities, and effects on economic welfare and financial stability. Simulation results indicate that current capital levels of Iranian banks are insufficient for achieving financial stability and welfare maximization, requiring increases to higher levels, that exceeding Basel III regulatory minimums to maintain adequate resilience against macroeconomic volatility and prevent bank bankruptcy and exit. Technology shocks generate expansionary effects on output, consumption, and investment variables while improving economic welfare, whereas liquidity shocks create contractionary pressures reducing these key economic indicators and welfare levels. Interest rate shocks produce moderate and largely temporary effects across macroeconomic variables, with limited long-term impact on economic welfare and minimal influence on bank bankruptcy probability. The shock analyses demonstrate the critical importance of macroeconomic conditions in bank capital management decisions, economic welfare preservation, and bank exit prevention, with liquidity shocks showing the most substantial explanatory power for capital requirement variations. The study contributes to both theoretical understanding and practical application by extending DSGE modeling to incorporate banking sector capital dynamics, bankruptcy mechanisms, and welfare effects within an emerging market context, thereby offering a more comprehensive framework for economic capital determination. Our findings suggest that Iranian banks need to maintain higher capital levels to provide adequate stability and risk absorption capacity for Iranian banks under diverse economic shock conditions.