A Comprehensive Study of Liquidity Risk Management in the Banking Industry: Identifying and Categorizing Components Using Meta-Synthesis Method

Maede Khezrian (), Hamed Naderi () and Mohammad Ali Rastegar ()
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Maede Khezrian: Master's student in financial engineering, University of Tarbiat Modares
Hamed Naderi: Ph. D. student of Industrial Engineering, University of Tarbiat Modares
Mohammad Ali Rastegar: Assistant Professor of Financial Engineering, University of Tarbiat Modares

Quarterly Journal of Applied Theories of Economics, 2023, vol. 10, issue 2, 65-96

Abstract: Liquidity risk is caused by the inability of a bank to pay debts on time, fulfil obligations, or the inability to expand the portfolio of high-yielding assets at a conventional cost. In other words, when a bank does not have sufficient liquidity, it is not able to obtain sufficient funds quickly and at a reasonable cost by increasing debts or converting assets, which will affect the bank's profitability. It can be said that the main cause of liquidity risk in banks is the mismatch between the amount and maturity of debts and assets, resulting in a negative liquidity gap. In this regard, institutions and banks used different approaches, including the approaches of the Wing Committee, to assess liquidity risk. This research aims to review liquidity risk management in the banking industry. In this research, content analysis of previous research has been done using the meta- Synthesis method and Sandelowski and Barroso's seven-stage model. Using the meta-Synthesis method, 242 related research between 2000 and 2023 were extracted from reliable scientific databases. For this purpose, after analyzing the research, 41 researchers have been selected. This research has provided a comprehensive framework for better risk management in the banking industry. This framework includes 5 main categories, which are liquidity risk data, liquidity risk factors, liquidity risk assessment, liquidity risk guidelines and liquidity risk management, 12 subcategories, 104 concepts and 175 codes. The results of this research can be a basis for better use of liquidity risk management in the banking industry. Also, the results were confirmed based on the opinion of experts with a Kappa index of 0.738

Keywords: Liquidity Risk; Liquidity Risk Management; Liquidity Coverage Ratio; Net Stable Funding Ratio; Meta-Synthesis (search for similar items in EconPapers)
JEL-codes: G21 G28 G32 G33 (search for similar items in EconPapers)
Date: 2023
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