Do banks change their liquidity ratios based on network characteristics?

Ardekani, Aref Mahdavi; Distinguin, Isabelle; Tarazi, Amine

doi:10.1016/j.ejor.2020.02.011

Cited by 18 publications

(12 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lack of bilateral financial exposure data has also led to simulations of financial networks using several assumptions. Examples are entropy maximisation methods (Castrén and Rancan 2014;Mistrulli 2011), correlations or Granger causality methods based on stock price data (see Billio et al 2012;Diebold and Yılmaz 2014;Hautsch et al 2015) and sparse network reconstruction models (Torri et al 2018;Anand et al 2015;Mahdavi Ardekani et al 2020). Simaan et al (2020) proposes a quite novel approach to estimate hidden networks from the interbank market using correlation-based statistical filtering methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

et al. 2021

View full text Add to dashboard Cite

We analyse systemic risk in the core global banking system using a new network-based spectral eigen-pair method, which treats network failure as a dynamical system stability problem. This is compared with market price-based Systemic Risk Indexes, viz. Marginal Expected Shortfall, Delta Conditional Value-at-Risk, and Conditional Capital Shortfall Measure of Systemic Risk in a cross-border setting. Unlike paradoxical market price based risk measures, which underestimate risk during periods of asset price booms, the eigen-pair method based on bilateral balance sheet data gives early-warning of instability in terms of the tipping point that is analogous to the R number in epidemic models. For this regulatory capital thresholds are used. Furthermore, network centrality measures identify systemically important and vulnerable banking systems. Market price-based SRIs are contemporaneous with the crisis and they are found to covary with risk measures like VaR and betas.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Moreover, it aligns with previous research that emphasizes the importance of minimum correlated liquidity shocks and longterm lending relationships in shaping the interbank connections, as suggested by Cocco et al (2009), Chiu et al (2019) and Craig and Von Peter (2014) in their exploration of the coreperiphery structure and tiering properties of the interbank network. Ardekani et al (2020) use the MD algorithm to construct their interbank exposure network and investigate the connection between interbank network characteristics and liquidity ratios of European banks. In line with their methodology, I adopt the MD algorithm proposed by Anand et al (2015) to simulate the bilateral exposure network.…”

Section: Interbank Networkmentioning

confidence: 99%

“…The Basel III capital and liquidity requirements are, however, independent of the banks' network topology or the quality of banks' interconnectedness in the interbank network. Ardekani et al (2020) provide evidence that banks' decisions regarding liquidity ratios are contingent upon their interbank network characteristics. Moreover, Distinguin et al (2013), Fu et al (2016) andHorv ath et al (2014) emphasize the existence of a causal relationship that flows from liquidity to capital.…”

Section: Introductionmentioning

confidence: 99%

“…This study is also related to literature on bank network topology, highlighting the role of interbank network connectedness in bank liquidity risk, systemic risk and the contagion of financial shocks (Ardekani et al, 2020;Borges et al, 2020;Capponi and Chen, 2015;Glasserman and Young, 2015;Huang et al, 2016;Paltalidis et al, 2015;Souza et al, 2015). To assess banks' interconnectedness and access to wholesale liquid funds, this study uses network topology statistics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Liquidity, interbank network topology and bank capital

Ardekani

2023

RAF

Self Cite

View full text Add to dashboard Cite

Purpose While previous literature has emphasized the causal relationship from liquidity to capital, the impact of interbank network characteristics on this relationship remains unclear. By applying the interbank network simulation, this paper aims to examine whether the causal relationship between capital and liquidity is influenced by bank positions in the interbank network. Design/methodology/approach Using the sample of 506 commercial banks established in 28 European countries from 2001 to 2013, the author adopts the generalized method of moments simultaneous equations approach to investigate whether interbank network characteristics influence the causal relationship between bank capital and liquidity. Findings Drawing on a sample of commercial banks from 28 European countries, this study suggests that the interconnectedness of banks within interbank loan and deposit networks shapes their decisions to establish higher or lower regulatory capital ratios in the face of increased illiquidity. These findings support the implementation of minimum liquidity ratios alongside capital ratios, as advocated by the Basel Committee on Banking Regulation and Supervision. In addition, the paper underscores the importance of regulatory authorities considering the network characteristics of banks in their oversight and decision-making processes. Originality/value This paper makes a valuable contribution to the current body of research by examining the influence of interbank network characteristics on the relationship between a bank’s capital and liquidity. The findings provide insights that add to the ongoing discourse on regulatory frameworks and emphasize the necessity of customized approaches that consider the varied interbank network positions of banks.

show abstract

“…[ 63 ] revealed that bank connections within a network are important to understand how banks set their liquidity ratios. Other scholars concentrate on the type of deposits to enhance liquidity.…”

Section: Literature Reviewmentioning

confidence: 99%

Key determinants of deposits volume using CAMEL rating system: The case of Saudi banks

AL-Najjar

Assous

2021

PLoS ONE

View full text Add to dashboard Cite

CAMEL is considered one of the well-known banking rating systems used to build a proper bank ranking. In our paper, we investigate the CAMEL rating for Saudi banks, which is considered the second largest banking sector in GCC. The Saudi banking sector consists of 11 banks and is the leading sector in the Saudi stock index (TASI). In this research, we aim to determine the ranking of Saudi banks according to CAMEL composite and CAMEL overall ratings and explore the effects of these ratings on banks’ total deposits for the period from 2014 to 2018. The methodology involves four phases. In the first phase, we calculate the key financial ratios of CAMEL’s composites for each bank. In the second phase, we rank the banks from 1 to 11 to each one of CAMEL’s composites for each bank per year. In the third phase, we rank Saudi banks according to CAMEL composite and CAMEL overall. Finally, in the fourth phase, we run a regression model using CAMEL financial ratios rank as independent variable and banks’ total deposits as a dependent variable. Using the stepwise regression method, the results indicated that the best regression model has an adjusted R2 of 73.4% and a standard error of around 0.58. The results further indicated that capital measured by CAR, management as an efficiency ratio, earning with ROE proxy, and liquidity as loans to deposits have positive effects on banks’ total deposits. Meanwhile, earnings as net interest income to net revenue and liquidity calculated by CASA have a negative effect on banks’ total deposits. Finally, asset quality ratios and the rest of the ratios have no significant effect on banks’ total deposits.

show abstract

Do banks change their liquidity ratios based on network characteristics?

Cited by 18 publications

References 66 publications

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods

Liquidity, interbank network topology and bank capital

Key determinants of deposits volume using CAMEL rating system: The case of Saudi banks

Contact Info

Product

Resources

About