Bank Liquidity Risk Research Articles

Bank liquidity risk in participatory banks. Which perspectives for Moroccan market ?

Bank liquidity risk in participatory banks. Which perspectives for Moroccan market ?

Factors Affecting Liquidity Risk in Islamic Banking in Pakistan

This study aims to examine the influence of market cost of funds, credit risk, profitability, and size on the funding strategy and liquidity risk in Islamic banking. The analysis utilizes quarterly aggregated time series data on Islamic banking from September 2006 to June 2022. All variables are found to be integrated into order one, indicating they are I(1). The study employs the Vector Error Correction Model (VECM) to investigate the short-run and long-run relationships. The findings indicate that size and credit risk are positively associated with liquidity risk. Furthermore, credit risk demonstrates a long-term co-movement with liquidity risk. Additionally, the results reveal a negative association between profitability and funding strategy, suggesting that higher profitability is associated with a more conservative funding approach. Lastly, the cost of funds exhibits a negative association with funding strategy, implying that lower market cost of funds is linked to a more aggressive funding strategy.‎‎

Digital Transformation and Liquidity Risk of Commercial Banks in Kenya

There are important trade-offs and synergies between digital transformation and financial stability. Poorly implemented digital transformation policies can impair bank liquidity. Also, there may be essential synergies brought by the broad use of digital services which help financial institutions diversify risk and aid financial stability. This study analyzed the effect of digital transformation on the liquidity risk of commercial banks, intending to establish a significant relationship between them. The specific objectives were to analyze the effect of Branch networks, ATMs, Agents and Mobile banking on the liquidity risk of commercial banks in Kenya. The research design was explanatory non–experimental. The target population included 42 commercial banks in Kenya and the study used secondary data. Descriptive statistics were used to establish the trend of digital services and liquidity risk of commercial banks while inferential statistics were used for testing the hypotheses. The results revealed that digital services had a statistically significant effect on the liquidity risk of commercial banks in Kenya during the study period between2007-2015. Increase in Branches, ATMs, Agents and Mobile banking were found to support liquidity levels (synergy) due to increased deposit mobilization and access to credit. Therefore, the study recommends increasing the banking customers, advancing affordable and accessible banking services to disadvantaged groups in different regions in the country. Reforms in financial sector should aim at increasing financial access through digital finance which is a cost cutting measure. Keywords: Digital services, Financial Inclusion, Unbankable Stability, Synergy, Trade-off

Systematic Market Risk Adjusted for Liquidity Cost in the Banking Sector

The emergence of one or more risks in the financial markets during a specific period causes a financial crisis. Financial crises impact financial stability, which is a key concern for all financial authorities, including central banks. One way to mitigate the risks any economy faces is to understand the origin of risk and how it spreads through the financial system. Liquidity risk goes hand-in-hand with market risk, as they affect each other during a crisis. After the 2007-2008 global financial crisis, banks showed that they need monitoring and efficient liquidity management during both stress and normal conditions; that is, they require better integration of bank liquidity and market risk management. In this study, we present a new methodology to forecast the systematic market risk adjusted for liquidity cost based on the Conditional Value at Risk (CoVaR) risk measure and asymmetric conditional copulas. We analyze a sample of international banks based on asset size in the US, EU, and Asia. Our hypothesis confirms that liquidity risk goes hand-in-hand with market risk, as they affect each other during a crisis. The results show dependence in the tails of the banks’ returns and the market returns. These are very high generally, and even higher for the Covid-19 period than for other periods. The effect of both market risk and liquidity risk on banks during a crisis period is less than in non-crisis periods because banks are well informed institutions and can anticipate a financial crisis and mitigate risk, which explains our results.

Research on Credit Risk of Corporate Bonds and Bank Liquidity Risk

Research on Credit Risk of Corporate Bonds and Bank Liquidity Risk

Security Optimization Management for loT-Assisted Bank Liquidity Risk Emergency Using Big Data Analytic-Based Case Reasoning

In modern times, financial institutions are the core carrier of efficient operation of financial markets. With the continuous development of financial models such as IoT (Internet of Things) finance, commercial banks have made many attempts in the integration and innovation of finance and logistics, but they have also increased the types and complexity of risks they face while improving financing efficiency. It has the characteristics of great destructiveness, strong infectivity, and high complexity. The establishment of a perfect emergency security optimization management for early warning of bank liquidity risk is an important part of timely detection and effective management of liquidity risk. In order to enable decision makers to accurately use the effective disposal of similar liquidity risk emergencies as a reference for decision-making, this paper studies the generation method of emergency security plan for bank liquidity risk using big data analytic-based case reasoning. Firstly, analyze the characteristics of various types of bank liquidity emergencies a, and identify the key risk points, and form the accident case database. Secondly, carry out the interval division according to the different numerical distribution characteristics of the indicators, and calculate the repeatability of the involved stages. Finally, calculate the comprehensive similarity to obtain the emergency security plan for reference. At the same time, taking A commercial bank as an example, verify the effectiveness of the method by using the constructed case-based reasoning model to generate emergency security plans intelligently. It provides reference for commercial bank liquidity risk management.

A study on the Relationship between Liquidity Risk and Financial Performance of Libyan Commercial Banks

Aims and Scope | Author Guidelines | Current Issue | Archive | Indexing | Editorial Board | Contact Us Issue 03 | Volume 5 | 2022 Title: A Study on Association of Liquidity Risk and Financial Performance in Libyan Commercial Banks Author(S): Khaled Mohamad A. Masli* and S. M Ferdous Azam Abstract: Financial performance analysis … Continue reading "International Tourism and Hospitality Journal (ITHJ)"

Determinants of Bank Liquidity: Empirical Evidence on Ethiopian Commercial Banks

Liquidity management is considered as one of the top priorities in banks to ensure their ability to reach funds when needed in order to meet their customers' demands and meet their obligations at time. Therefore, this study aims to identify determinants of liquidity for commercial banks in Ethiopia. In order to achieve this aim, banks’ balance sheets and income statements was analyzed in order to compute study variables. Liquidity, which is the dependent variable of study, expressed by two liquidity ratios as: liquid assets to total deposit (L1), and loans to deposits (L2).Therefore, the purpose of this research was to identify the factors determine Ethiopian commercial banks liquidity. The study has categorized the independent factors into bank specific factors and macro-economic factors. The bank specific factors include management efficiency, deposit, Bank Size, Capital adequacy, Profitability, asset quality, and loan growth. While, the macroeconomic factors include short term interest rate, interest rate spread, interest rate, Gross Domestic Product (GDP), general inflation and unemployment rate. The panel data was used for the sample of nine commercial banks in Ethiopia from 2007 to 2017 year and estimated using Fixed Effect Model (FEM), data was present by using descriptive statistics and the balanced correlation and regression analysis for liquidity ratios was conducted. The findings of the study show that, asset quality, GDP, and loan growth had statistically significant and negative relationship with banks’ liquidity. profit and unemployment rate had statically positive significant impact on liquidity measured by variable L1.whereas interest rate spread and bank size had statically negative significant impact on L1 On the other hand capital adequacy had statistically significant and positive relationship with banks’ liquidity measured by variable L2. While, inflation rate, and management efficiency had no statistically effect on bank liquidity. The study generally stresses the importance of internal management of liquidity risk of banks on a continuous basis, and specifically recommends balancing the bank's deposit taking and lending structure on the one hand and maintaining liquidity on the other. It also recommends to strengthening banks capital structure and control over operating expenses. Moreover, banks in Ethiopia should not only be concerned about internal structures and policies, but they must consider both the government regulation and the macroeconomic environment together in developing strategies to improve the liquidity position of the banks. Key w ords: Liquidity, Commercial banks, assets, loan DOI: 10.7176/RJFA/13-11-03 Publication date: June 30 th 2022

DETERMINANTS OF SYSTEMATIC AND UNSYSTEMATIC LIQUIDITY RISK IN ISLAMIC BANKS

This study examines whether systematic (macroeconomic) and unsystematic (bank specific) factors determine liquidity risk in Islamic banks. The study employs a sample of Islamic banks from Pakistan, Qatar, Malaysia, UAE, Bangladesh, Bahrain, and Saudi Arabia over the period 2008 – 2019. Using Least Square estimation methods to estimate the model separately for each country, we find the results to be mixed and different across countries. The results also show that non-performing loans, bank size, leverage ratio and return on assets are key unsystematic drivers in determining the liquidity risk of Islamic banks. This study points out the fragility of Islamic banks in relation to managing liquidity risk.

Intelligent Evaluation and Early Warning of Liquidity Risk of Commercial Banks Based on RNN.

With the downward pressure of China's economy and the impact of the epidemic, the accumulated market risk has increased the liquidity pressure of the banking industry, and the mismatch between deposit maturity and loan maturity is the main cause for the increase of liquidity risk. The twenty-first century is the era of rapid and in-depth development of data management technology. The explosive growth of massive financial data makes the information data related to the liquidity risk of commercial banks present the characteristics of complexity, diversity, and heterogeneity. The traditional risk early warning model cannot deal with the influence between a large number of influencing factors and the nonlinear factors of commercial bank liquidity risk. Based on this transformation, the circular neural network model is introduced into the field of liquidity risk early warning of commercial banks from the perspective of the mismatch risk of financing maturity of commercial banks, and the driving factors and risk warning signs of liquidity risk of commercial banks are further analyzed from the institutional level, policy level, industry level, and micro commercial bank level. This paper uses network crawler technology, text analysis, and grounded analysis technology to intelligently identify the liquidity risk of commercial banks and establishes an early warning index system based on the influencing factors of commercial banks and internal liquidity risk. Also, it constructs an intelligent early warning model of commercial bank liquidity risk based on deep learning and uses the data of commercial banks from 2000 to 2020 for early warning. The results show that the constructed model has high accuracy, which can provide support for banks and relevant government departments to formulate and resolve bank liquidity risk.

Vulnerability of Islamic banking in ASEAN

PurposeThis research evaluated the impact of credit risk, liquidity risk, profitability, economic growth and good governance on the vulnerability of the Islamic banking system in the Association of Southeast Asian Nations (ASEAN).Design/methodology/approachThe panel regression analysis was used to obtain data from five ASEAN countries that had operated Islamic banks from 2010 to 2019.FindingsThe results obtained from the vulnerability model indicated that bank liquidity risk, profitability and good governance have significant impacts on vulnerability. Conversely, credit risk and economic growth showed an insignificant effect on susceptibility. Good governance helps increase investment attractiveness for economic growth and development in Islamic banks in ASEAN.Research limitations/implicationsSome of the limitations of this research include its focus on the vulnerability of Islamic banks in ASEAN countries. The average value of six indices is used as a single index per country with good governance. Therefore, further research needs to consider using all six indices of good governance as factors affecting the vulnerability of Islamic banks, such as control of corruption, government effectiveness, political stability, absence of violence, regulatory quality, the rule of law voice and accountability.Practical implicationsThis research describes banking financial circumstances and their internal activities. Furthermore, it helps managers or banking practitioners in the proper management of finance, specifically at the vulnerability level, to aid in the early detection of crisis to enable early aversion or minimal impact.Social implicationsThis research is expected to assist governments in ASEAN countries to establish public policies and build good governance to increase investment interest in the Islamic banking industry.Originality/valueThis research is the author's first attempt at discussing the issues of bank vulnerability related to good governance faced by the Islamic banking system in ASEAN.

Funding liquidity risk and asset risk of Indonesian Islamic rural banks

Funding liquidity risk and asset risk of Indonesian Islamic rural banks

Analysis of the Effect of Non-Performing Loan, Return on Assets, Return on Equity and Size on Banking Liquidity Risk (Case Study on Conventional Banks Registered in IDX period 2016 – 2020)

In the economy of a country, banking has a big part to do. Banking plays an important role in lending in public and private areas. The function of banking as an intermediary, which the bank is useful as an intermediary between the parties who are sufficient to those in need. This research is focused on areas that have an effect on liquidity risk in banks. The purpose of this research is nothing but to analyze the securities of NPL, ROA, ROE and Size to liquidity risk in banks listing in IDX for the period 2016-2020. The samples used in the research of all banks, both state-owned (SOE) and National Private Banks in Indonesia are recognized in BI from 2016 to 2020. From the criteria obtained by 40 banks, the method used is purposive sampling. The method of analysis used in this research is linear regression, which is tested through classical assumptions with normality, multicollinearity, autocorrelation and heteroscedasticity. The results showed that roa and ROE variables have a good and significant effect on liquidity risk. Medium variable NPL and Size have an adverse and insignificant effect on liquidity risk.

Financial Risk Control Model Based on Deep Neural Networks

Under the background of global economic integration, commercial banks are facing more and more complex business environment. As one of the major financial risks faced by commercial banks, liquidity risk determines and reflects the safety and profitability of bank operation. Based on joint-stock commercial banks as the research object, this paper, respectively, from the angle of the static and dynamic measurements and projections for liquidity risk and based on the current situation of four joint-stock commercial banks liquidity level study, tries to explore the change law of commercial banks liquidity risk and financial risk control of commercial bank and puts forward reasonable suggestions. In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects of the single evaluation method but also improve the data accessibility by using the qualitative data and quantitative data of AHP. In the aspect of financial risk control system, this paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business model process, and the experience of practical workers.

Interbank borrowing and bank liquidity risk

AbstractTo avoid illiquidity spillovers and basis risk in swaps, interbank lenders are especially cautious about whether interbank borrowers can meet their claims. We examine whether the incentive of interbank lenders to penalize risky borrowers can reduce borrowers' liquidity risk taking. We find that interbank borrowers, especially small and medium banks, manage their liquidity risks more prudently than their counterparts. This phenomenon is especially significant for borrowers with high information asymmetry, low liquidity buffers, and high funding gaps. Our results suggest that interbank exposure reduces the asset, funding, and off‐balance‐sheet liquidity risks of small and medium borrowing banks, and can therefore supplement regulatory liquidity requirements, which target only the largest banks.

The Influence of Banking Risk on Efficiency: The Moderating Role of Inflation Rate

The increasing business activities of state-owned banks in Indonesia increases risks. This circumstance can impact the level of state-owned banks’ efficiency. Thus, this research analyses the influence of banking risks on the state-owned banks’ efficiency in Indonesia from 2016 to 2019. Moreover, inflation as one of the macroeconomic factors may also affect the relationship between banking risks and efficiency; hence, this research also examines the inflation role as a moderating variable of this relationship. The samples of this research are all state-owned banks in Indonesia. Ordinary Least Squares (OLS) regression analysis shows that liquidity risk and credit risk have positive and significant influences on efficiency. However, inflation as a moderating variable has no significant influence on efficiency. Inflation also fails to moderate the relationship between liquidity risk and efficiency of state-owned banks in Indonesia. Nevertheless, inflation successful moderates the relationship between credit risk and efficiency.

How Does Bank Competition Affect Banking Stability? The Case of an Emerging Market

This paper examines the role of interbank competition on banking stability in Jordan, an emerging market. Using competition estimates measured by Lerner index, the paper examines the impact of competition on two important dimensions of banking stability, namely, credit risk and liquidity risk. The empirical results suggest that the effect of banking competition on stability differs according to the dimension of stability. After controlling for bank-level control variables and macroeconomic conditions, the empirical results of the impact of market power measured by Lerner index on credit risk show that an increase in price competition leads to more risk taking from banks. On the other hand, the impact of price competition on the second dimension of banking stability (liquidity risk) implies that an increase in price competition improves the liquidity position of banks. However, these results hold only for the sample of non-Islamic banks. Islamic banks may face wider mismatch between its assets and liabilities due to its inability to use conventional debt instruments offered by the market and hence it keeps larger cash buffers to compensate for this extra risk.

The effect of financial repression policy on bank liquidity risk (evidence from Central Bank of Iran)

The effect of financial repression policy on bank liquidity risk (evidence from Central Bank of Iran)

The effect of financial repression policy on bank liquidity risk (evidence from Central Bank of Iran)

The effect of financial repression policy on bank liquidity risk (evidence from Central Bank of Iran)

Research on liquidity risk of commercial bank – from the view of comparison of Chinese and American commercial banks

The liquidity risk of commercial banks has become an important driver of the major risks in the modern economic system. This paper synthesizes the off balance sheet items which are often ignored in traditional bank liquidity researches, and uses the method of tracking and comparative analysis in different window periods to explore the liquidity changes and possible risks of Chinese commercial banks before, during and after the financial crisis. It is found that traditional loan projects, committed loan projects and demand deposits are important drivers of liquidity risk; Although the liquidity level of China’s banks is high, due to the high demand deposit rate, low core capital ratio, rapid loan growth and high non-performing rate, and the lack of risk prevention awareness, liquidity risk is still a major risk that China’s commercial banks need to face.