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 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.

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

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.

scholarly journals EVALUATING THE ACCURACY OF TAIL RISK FORECASTS FOR SYSTEMIC RISK MEASUREMENT

2018 ◽  
Vol 13 (02) ◽  
pp. 1850009 ◽  
Author(s):  
CHRISTIAN BROWNLEES ◽  
GIUSEPPE CAVALIERE ◽  
ALICE MONTI
Keyword(s):  
Mean Square Error ◽  
Financial Institutions ◽  
Loss Function ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measurement ◽  
Loss Functions ◽  
Conditional Value At Risk ◽  
Mean Square ◽  
Tail Risk

In this paper, we address how to evaluate tail risk forecasts for systemic risk (SRISK) measurement. We propose two loss functions, the Tail Tick Loss and the Tail Mean Square Error, to evaluate, respectively, Conditional Value-at-Risk (CoVaR) and MES forecasts. We then analyse CoVaR and MES forecasts for a panel of top US financial institutions between 2000 and 2012 constructed using a set of bivariate DCC-GARCH-type models. The empirical results highlight the importance of using an appropriate loss function for the evaluation of such forecasts. Among other findings, the analysis confirms that the DCC-GJR specification provides accurate predictions for both CoVaR and MES, in particular for the riskiest group of institutions in the panel (Broker-Dealers).

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