Interbank systemic risk network in an emerging economy

IF 3.6 Q1 BUSINESS, FINANCE
Molla Ramizur Rahman, Arun Kumar Misra, Aviral Kumar Tiwari
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引用次数: 0

Abstract

Purpose

Interconnections among banks are an essential feature of the banking system as it helps in an effective payment system and liquidity management. However, it can be a nightmare during a crisis when these interconnections can act as contagion channels. Therefore, it becomes essentially important to identify good links (non-contagious channels) and bad links (contagious channels).

Design/methodology/approach

The article estimated systemic risk using quantile regression through the ΔCoVaR approach. The interconnected phenomenon among banks has been analyzed through Granger causality, and the systemic network properties are evaluated. The authors have developed a fixed effect panel regression model to predict interconnectedness. Profitability-adjusted systemic index is framed to identify good (non-contagious) or bad (contagious) channels. The authors further developed a logit model to find the probability of a link being non-contagious. The study sample includes 36 listed Indian banks for the period 2012 to 2018.

Findings

The study indicated interconnections increased drastically during the Indian non-performing asset crisis. The study highlighted that contagion channels are higher than non-contagious channels for the studied periods. Interbank bad distance dominates good distance, highlighting the systemic importance of banking network. It is also found that network characteristics can act as an indicator of a crisis.

Originality/value

The study is the first to differentiate the systemic contagious and non-contagious channels in the interbank network. The uniqueness also lies in developing the normalized systemic index, where systemic risk is adjusted to profitability.

新兴经济体的银行间系统风险网络
目的银行之间的相互联系是银行系统的一个基本特征,因为它有助于有效的支付系统和流动性管理。然而,在危机期间,当这些相互联系成为传染渠道时,这可能会成为一场噩梦。因此,识别好的联系(非传染渠道)和坏的联系(传染渠道)就变得尤为重要。文章通过格兰杰因果关系分析了银行间的相互联系现象,并对系统网络特性进行了评估。作者建立了一个固定效应面板回归模型来预测相互关联性。盈利能力调整后的系统性指数被用来识别好的(非传染性)或坏的(传染性)渠道。作者进一步开发了一个 logit 模型,以找出非传染性联系的概率。研究样本包括 2012 年至 2018 年期间的 36 家印度上市银行。研究结果研究表明,在印度不良资产危机期间,相互联系急剧增加。研究强调,在研究期间,传染渠道高于非传染渠道。银行间的不良距离大于良好距离,凸显了银行网络的系统重要性。该研究首次区分了银行间网络中的系统性传染渠道和非传染渠道。其独特之处还在于开发了归一化系统性指数,将系统性风险调整为盈利能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.30
自引率
0.00%
发文量
18
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