scholarly journals Banks’ Noninterest Income and Systemic Risk

2020 ◽  
Author(s):  
Markus K Brunnermeier ◽  
Gang Nathan Dong ◽  
Darius Palia
Keyword(s):  
Economic Impact ◽  
Systemic Risk ◽  
Tail Risk ◽  
Interest Income ◽  
Editorial Decision ◽  
Three Components

Abstract This paper finds noninterest income is positively correlated with the total systemic risk for U.S. banks. Decomposing total systemic risk into three components, we find that noninterest income is positively related to a bank’s tail risk, positively related to a bank’s interconnectedness risk, and an insignificantly related to a bank’s exposure to macroeconomic and finance factors. We also find that noninterest income is more volatile and negatively related to interest income. Finally, we find trading and other noninterest income to be positively correlated with systemic risk. Other noninterest income, compared with trading income, has a slightly larger economic impact. (JEL G01, G18, G20, G21, G32, G38) Received October 31, 2019; editorial decision February 3, 2020 by Editor Andrew Ellul.

Banks' Non-Interest Income and Systemic Risk

2019 ◽  
Author(s):  
Markus Konrad Brunnermeier ◽  
Gang Nathan Dong ◽  
Darius Palia
Keyword(s):  
Systemic Risk ◽  
Interest Income

Banks’ Non-Interest Income and Systemic Risk

2012 ◽  
Author(s):  
Markus K. Brunnermeier ◽  
Gang (Nathan) Dong ◽  
Darius Palia
Keyword(s):  
Systemic Risk ◽  
Interest Income

Tail risk and systemic risk of finance and technology (FinTech) firms

2022 ◽  
Vol 174 ◽  
pp. 121191
Author(s):  
Sajid M. Chaudhry ◽  
Rizwan Ahmed ◽  
Toan Luu Duc Huynh ◽  
Chonlakan Benjasak
Keyword(s):  
Systemic Risk ◽  
Tail Risk

scholarly journals Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

2020 ◽  
Vol 54 ◽  
pp. 101248 ◽  
Author(s):  
Weiping Zhang ◽  
Xintian Zhuang ◽  
Jian Wang ◽  
Yang Lu
Keyword(s):  
Systemic Risk ◽  
Tail Risk

scholarly journals What Factors Drive the Banks Systemic Risk among South Asian Countries

2021 ◽  
Vol 7 (4) ◽  
Author(s):  
Raheel Mumtaz ◽  
Quaisar Ijaz Khan ◽  
M.Farooq Rehan
Keyword(s):  
South Asian ◽  
Systemic Risk ◽  
Panel Data Analysis ◽  
Banking System ◽  
Annual Reports ◽  
Asian Countries ◽  
Leverage Ratio ◽  
Interest Income ◽  
Income Ratio ◽  
Risk Contribution

Purpose: This study designs to examine the determinants (size, liquidity ratio, leverage ratio, deposit ratio, asset growth, net interest income ratio and return on asset ratio) of bank’s systemic risk. We use the data of listed commercial banks of the South Asian countries (Pakistan, Bangladesh, and India). Design/Methodology/Approach: The sample consists 30 banks from Bangladesh, 87 banks from India and 22 banks from Pakistan. This study covers the period from 2006 to 2018. The data is collected from the published annual reports of banks and stock exchanges of respective country. The panel data analysis is performed for the estimation of research models. Findings: The findings demonstrate that larger banks contribute lower in the systemic risk of banks. Additionally, highly liquid banks enhance the systemic risk of the banking system. Moreover, the banks with greater reliance on the deposits, net interest income and with high return on asset reduce the systemic risk contribution of the banks. Implications/Originality/Value: This study provides the justification to devise the banking policies like enhance the proportion of liquidity among assets, reliance on net interest income and promote the financing needs through deposits to limit the systemic risk contribution of the banking system.                                                            

scholarly journals Determinants of Systemic Risk of Banks in India

2019 ◽  
Vol 11 (1) ◽  
pp. 272
Author(s):  
Mihir Dash
Keyword(s):  
Public Sector ◽  
Private Sector ◽  
Systemic Risk ◽  
Fixed Effects ◽  
Capital Adequacy ◽  
Return On Assets ◽  
Book Value ◽  
Public Sector Banks ◽  
Interest Income ◽  
Systemic Impact

This study examines the determinants of systemic risk for banks in India. The independent variables considered for the study include the sector, bank size, return on assets, beta, leverage, capital adequacy, non-performing assets, price to book value, deposits, loans & advances, investments, net interest income, and non-interest income. A mixed panel regression model was applied, with bank fixed effects and year random effects.The results of the study indicate that public sector banks have a much higher level of systemic impact than private sector banks. Further, the determinants of systemic impact are different for public sector and private sector banks. The systemic impact of public sector banks was positively related with size and negatively related with price to book value ratio and investments to total assets ratio, while the systemic impact of private sector banks was negatively related with return on assets and positively related with beta and net interest income to total funds ratio.

CoMargin

2017 ◽  
Vol 52 (5) ◽  
pp. 2183-2215 ◽  
Author(s):  
Jorge A. Cruz Lopez ◽  
Jeffrey H. Harris ◽  
Christophe Hurlin ◽  
Christophe Pérignon
Keyword(s):  
Systemic Risk ◽  
Market Participant ◽  
Tail Risk ◽  
Central Counterparties ◽  
The Stability

We present CoMargin, a new methodology to estimate collateral requirements in derivatives central counterparties (CCPs). CoMargin depends on both the tail risk of a given market participant and its interdependence with other participants. Our approach internalizes trading externalities and enhances the stability of CCPs, thus reducing systemic risk concerns. We assess our methodology using proprietary data from the Canadian Derivatives Clearing Corporation that include daily observations of the actual trading positions of all of its members from 2003 to 2011. We show that CoMargin outperforms existing margining systems by stabilizing the probability and minimizing the shortfall of simultaneous margin-exceeding losses.

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