Bank Competition Using Networks: A Study on an Emerging Economy

Abstract

Interconnectedness among banks is a key distinguishing feature of the banking system. It helps mitigate liquidity problems but on the other hand, acts as a curse in propagating systemic risk at times of distress. Thus, as banks cannot function in isolation, this study uses the Contemporary Theory of Networks to examine banking competition in India for five distinct economic phases, emphasizing upon the Global Financial Crisis (GFC) and the ongoing COVID-19 pandemic. This paper proposes a Market Power Network Index (MPNI), which uses network parameters to measure banks’ market power. This network structure shows a formation of bank clusters that are involved in competition. Specifically, network properties, such as centroid, average path length, the distance of a node from the centroid, the total number of connections in the inter-bank market, and network density, do go on to explain banking competition. It is interesting to note that crisis periods witness a lower level of competition, with GFC bearing the least competition. The ongoing COVID-19 pandemic shows a lower trend, but it is of a higher magnitude than GFC. It was also found that big-sized, profitable, capital adequate, and public banks dominate the banking system. Notably, this study was conducted on a sample of 33 listed Indian banks from April 2008 to December 2020.

Interlinkages of market power, price and liquidity network in banks: evidence from an emerging economy

“…Several studies were conducted to understand the impact of network structure on systemic risk and stability of the financial system (Nier et al ., 2007; Elliott et al ., 2014; Battiston et al ., 2012). Studies have shown the construction of financial networks using the Granger non-causality (Billio et al ., 2012; Rahman and Misra, 2021), CoVaR (Adrian and Brunnermeier, 2011), Copula (Li et al ., 2018), time-varying VAR (Kimura et al ., 2003) and LASSO-VAR (Yi et al ., 2018) technique. The relationship between nodes and edges represents the financial network, a complex system of many banks and their respective links (Gao et al ., 2018).…”

“…(2019) explained the connectivity among systemically important financial institutions through the de-biased LASSO-VAR (DLVAR) method. In a more recent study, Rahaman and Misra (2021) developed a market power network index (MPNI) to estimate bank market power using network parameters, which helped to explore the formation of bank clusters involved in the competition.…”

A risk-neutral approach to the RAROC method of loan pricing using account-level data

“…Further, Economic Policy Uncertainty (EPU) influences bank loan rates where events like parliamentary elections enhance loan rates due to the associated policy uncertainty (Ashraf and Shen, 2019). However, bank competition also influences pricing, where bank competition can be studied using contemporary techniques implementing network models (Rahman and Misra, 2021). Systemic risk has become more prominent after the Global Financial Crisis (GFC), and financial contagion increases systemic risk in this interconnected banking environment.…”

Competitiveness in Banking Sector: A Systematic Literature Review

“…Other authors focused on entirely new methods of estimating competition in the banking sector (Dincer 2019, Rahman & Misra 2021. For example, in the research conducted by Rahman & Misra (2021), the authors used Market Power Network Index (MNPI) based on the view that banks do not exist in isolation.…”

“…Other authors focused on entirely new methods of estimating competition in the banking sector (Dincer 2019, Rahman & Misra 2021. For example, in the research conducted by Rahman & Misra (2021), the authors used Market Power Network Index (MNPI) based on the view that banks do not exist in isolation. Another new approach was implemented by Dincer (2019) to evaluate the level of competition and concentration using a multi-criteria decision-making approach within the fuzzy environment.…”