scholarly journals Impact of diversification on systemic risk of conventional banks listed on the Indonesia Stock Exchange

2020 ◽  
Vol 15 (4) ◽  
pp. 80-87
Author(s):  
Musa Fresno ◽  
Dewi Hanggraeni
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Stock Exchange ◽  
Conditional Value At Risk ◽  
Revenue Diversification ◽  
Variable Approach ◽  
Systemic Risks ◽  
Conventional Banks ◽  
The Impact

It is believed that bank diversification increases financial stability. However, several theories argue that diversification can trigger the spread of failure because of the increased interconnectivity between institutions. The aim of this study is to determine the impact of diversification on the systemic risk of banks. The sample of the study consists of 21 conventional banks listed on the Indonesia Stock Exchange from 2009 to 2018. The study uses firm-year fixed effect panel regression and an instrumental variable approach to examine how firm-specific variables determine the level of systemic risk. Diversification is measured by bank assets, funding, and revenue diversification. To measure the systemic risk, the Conditional Value-at-Risk (ΔCoVaR) methodology is applied. The results show that an increase in funding diversification leads to a decrease in ΔCoVaR, indicating that funding diversification exacerbates the level of systemic risk, whereas asset diversification and revenue diversification do not have significant effects on the level of systemic risk. The empirical findings suggest that the interconnectivity between banks should be reduced by limiting the diversification of funding in the banks to minimize their systemic risks.

scholarly journals How We Predict the Stability of Financial Sector: The Conditional Value at Risk Technique Approach

2020 ◽  
Author(s):  
David Kaluge
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Negative Impact ◽  
Stock Exchange ◽  
Banking System ◽  
Conditional Value At Risk ◽  
Negative Contribution ◽  
The Stability ◽  
The Impact

This study aims to identify the level of systemic risk of each bank and the financial linkages between banks in Indonesia. In this study, researcher uses 41 banks that have been actively traded on the Indonesia Stock Exchange in the period 2013-2018. The data of stock capitalization of banks are used as prices in a portfolio of banking system. The method used in this study is the CVaR (Conditional Value at Risk) method which was introduced by Adrian and Brunerrmeir in 2008. The equilibrium of the system is assumed reached at optimum portfolio of the system. At this situation each bank contribution to systemic risk is analyzed, as well as its impact onto it when there is a change in capitalization of a certain bank. The result shows the impact of bank onto systemic risk is not always follow its size in contribution the systemic risk. Due to covariance’s among banks are some positive and others are negative, some banks have negative contribution to systemic risk while others’ are positive. There are 4 banks that have different behavior. These banks have negative contribution to the systemic risk. These banks are BMRI, PNBN, PNBS and NAGA. The negative impact to systemic risk is dominated by BMRI as much as -0.17%, and by PNBN as much as -0.04%. There are 2 major banks that have contribution to systemic risk; BBCA (3,01% or Rp 59,1 trillion) and BBRI (0,54% Rp 10,62 trillion). However their impact on systemic risk are different. The parameters of impact on systemic for BBCA and BBRI are 14,99% and 52,94% respectively. Thus the stability of the system is more sensitive to the volatility of Bank Rakyat Indonesia (BBRI) than of Bank Central Asia (BBCA). Keywords: Systemic Risk, Financial Linkage, Value at Risk, Conditional Value at Risk, covariance banking

Shadow banking's effects in the Chilean financial system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Diana López Avilés ◽  
Paula Piñeira ◽  
Víctor Andrés Roco Cáceres ◽  
Felipe Vergara ◽  
Nicolas Araya
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Financial System ◽  
Conditional Value At Risk ◽  
Shadow Banking ◽  
Content Type ◽  
Relevant Effect ◽  
New Research ◽  
The Impact

PurposeThe Financial Stability Board (FSB) determined that entities classified as shadow banking are of a credit nature because they are capable of affecting the financial system through the entry and exit of capital. This study aims at measuring the impact of shadow banking in the systemic risk in Chile. A sample of 91 institutions (Run) belonging to the mutual funds was used, with a series showing a continuous behaviour between 2004 and 2018.Design/methodology/approachThe measurement is carried out using the conditional value at risk (CoVaR) methodology, which analyses the behaviour of an institution in a regular state against the same institution in a state of stress.FindingsThe results obtained reflect that liquidity mismatches do not have a relevant effect on the systemic risk, while the 2008 crisis does contribute to its decline.Originality/valueThere are less number of literature studies that apply statistical models regarding shadow banking, at least at a quantitative level, so this research is a beginning for other studies, supporting future authors in their new research as a basis.

scholarly journals Discovering Systemic Risks of China's Listed Banks by CoVaR Approach in the Digital Economy Era

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 180
Author(s):  
Jiang ◽  
Zhang
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Dynamic Models ◽  
Digital Economy ◽  
Conditional Value At Risk ◽  
Financial Industry ◽  
Systemic Risks ◽  
The World ◽  
Small Banks

The world has entered the digital economy era. As a developing country, China's banking industry plays an important role in the financial industry, and its size ranks first in the world. Therefore, it is of great significance to study the systemic risks of China's banks in the digital economy era. We first compare the traditional indicator approach and the market-based approach theoretically, and Conditional Value at Risk (CoVaR) model, a market-based approach, is considered to be an efficient way to discover systemic risk in different perspectives. Based on static and dynamic models, we evaluate the contributions of sixteen China's listed banks to the systemic risk. Furthermore, we model bank exposures, extend the models by considering extreme circumstance, and incorporate the effects of Fintech and non-bank financial institutions. The results show the levels of systemic risks and the corresponding systemic importance rankings vary in different time periods. We find that the contributions of some small banks to systemic risk are even higher than some big banks during the sample period. Moreover, the big banks face less risks than most of the small banks when the banking system is in distress. We make suggestions for improving financial supervision and maintaining financial stability.

Does securitization escalate banks’ sensitivity to systemic risk?

2020 ◽  
Vol 21 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Katerina Ivanov ◽  
Julia Jiang
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Supply And Demand ◽  
Conditional Value At Risk ◽  
Empirical Literature ◽  
Content Type ◽  
Holding Companies ◽  
Residential Mortgage ◽  
The Us ◽  
The Impact

Purpose The purpose of this paper is to test empirically the impact of asset securitization and sale activities as well as the holdings of sub-prime related securitized products on the US bank holding companies’ (BHC) exposure to systemic risk. Design/methodology/approach This paper adopts a robust econometric method to estimate the conditional value-at-risk as a measure of BHCs' institutional sensitivity to market crushes. Using the data over the period of 2004-2016, the study also uses OLS with robust standard errors and panel estimation with random effects as two alternative estimation techniques to assess the impact of securitization activities on the sensitivity of BHCs to systemic risk. Findings Residential mortgage and other forms of securitization activities are positively related to an increase in the US BHCs' sensitivity to systemic distress. The significant cross effects of both securitized loans and holdings of securitized products play a crucial role in determining risks in financial sector. Originality/value This study contributes to the empirical literature on the effects of securitization on BHCs' risk exposures in several ways. First, the paper considers the complexity of the bank's risk profile; it focuses on BHCs' individual sensitivity to systemic distress and its dependence on the size of securitization and assets sold activities considering both supply and demand sides of securitization. Second, the time horizon under investigation sheds a light on the relationship between securitization and banks' risk exposures including the pre-crisis, crisis and post-crisis periods.

Bank Geographic Diversification and Systemic Risk

2019 ◽  
Vol 33 (10) ◽  
pp. 4811-4838 ◽  
Author(s):  
Yongqiang Chu ◽  
Saiying Deng ◽  
Cong Xia
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Integration ◽  
Causal Effect ◽  
Conditional Value At Risk ◽  
Geographic Diversification ◽  
Banking Deregulation ◽  
Exogenous Shocks ◽  
Interstate Banking ◽  
The Impact

Abstract Exploiting staggered interstate banking deregulation as exogenous shocks to bank geographic expansion, we examine the causal effect of geographic diversification on systemic risk. Using the gravity-deregulation approach, we find that bank geographic diversification leads to higher systemic risk measured by the change in conditional value at risk ($\Delta$CoVaR) and financial integration (Logistic($R^{2}))$. Furthermore, we document that geographic diversification affects systemic risk via its impact on asset similarity. The impact of geographic diversification on systemic risk is stronger in BHCs located in states comoving less with the U.S. aggregate economy.

scholarly journals Comparing Different Systemic Risk Measures for European Banking System

2018 ◽  
Vol 12 (1) ◽  
pp. 35 ◽  
Author(s):  
Annalisa Di Clemente
Keyword(s):  
Systemic Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Risk Measures ◽  
Banking System ◽  
Expected Shortfall ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
European Banking ◽  
European Banks

This research examines and compares the performances in terms of systemic risk ranking for three different systemic risk metrics based on daily frequency publicly available data, specifically: Marginal Expected Shortfall (ES), Component Expected Shortfall (CES) and Delta Conditional Value-at-Risk (ΔCoVaR). We compute ΔCoVaR, MES and CES by utilizing EVT principles for modelling marginal distributions and Student’s t copula for describing the dependence structure between every bank and the banking system. Our objective is to attest whether different systemic risk metrics detect the same banks as systemically dangerous institutions with refer to a sample of European banks over the time span 2004-2015. For each bank in the sample we also calculate three traditional market risk measures, like Market VaR, Sharpe’s beta and the correlation between every bank and the banking system (European STOXX 600 Banks Index). Another aim is to explore the existence of a link among systemic risk measures and traditional risk metrics. In addition, the classification results obtained by the different risk metrics are compared with the ranking in terms of systemic riskiness (for European banks) calculated by Financial Stability Board (2015) using end-2014 data and collected in its list of Global Systemically Important Banks (G-SIBs). With refer to the entire sample period, we find a good coherence of ranking results among the three different systemic risk metrics, in particular between CES and ΔCoVaR. Moreover, we find for MES and ΔCoVaR a strong linkage with beta and correlation metrics respectively. Finally, CES metric shows the highest level of concordance with the list of G-SIBs by FSB with refer to European banks.

scholarly journals What Kind Of Systemic Risks Do We Face In The European Banking Sector? The Approach Of CoVaR Measure

2014 ◽  
Vol 14 (2) ◽  
pp. 114-124 ◽  
Author(s):  
Renata Karkowska
Keyword(s):  
Financial Institutions ◽  
Systemic Risk ◽  
Value At Risk ◽  
Banking Sector ◽  
Significant Risk ◽  
Conditional Value At Risk ◽  
The Public ◽  
Public Market ◽  
Systemic Risks ◽  
European Banking

Abstract We measure a systemic risk faced by European banking sectors using the CoVaR measure. We propose the conditional value-at-risk for measuring a spillover risk which demonstrates the bilateral relation between the tail risks of two financial institutions. The aim of the study is to estimate the contribution systemic risk of the bank i in the analyzed banking sector of a country in conditions of its insolvency. The study included commercial banks from 8 emerging markets from Europe, which gave a total of 40 banks, traded on the public market, which provided a market valuation of the bank’s capital. The conclusions are that the CoVaR seems to be a better measure for systemic risk in the banking sector than the VaR, which is more individual. And banks in developing countries in Europe do not provide significant risk for the banking sector as a whole. But it must be taken into account that some individuals that may find objectionable. Our results hence tend to a practical use of the CoVaR for supervisory purposes.

scholarly journals CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions

2020 ◽  
Vol 13 (11) ◽  
pp. 270
Author(s):  
Rui Ding ◽  
Stan Uryasev
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Financial System ◽  
Conditional Value At Risk ◽  
New Approach ◽  
Systemic Risks ◽  
Study Results ◽  
Risk Contribution

Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.

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

2021 ◽  
Author(s):  
Sheri Markose ◽  
Simone Giansante ◽  
Nicolas A. Eterovic ◽  
Mateusz Gatkowski
Keyword(s):  
Early Warning ◽  
Systemic Risk ◽  
Value At Risk ◽  
Risk Measures ◽  
Market Price ◽  
System Stability ◽  
Conditional Value At Risk ◽  
Centrality Measures ◽  
Global Banking ◽  
Pair Method

AbstractWe 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.

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