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

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.

2020 ◽  
Vol 15 (4) ◽  
pp. 80-87
Author(s):  
Musa Fresno ◽  
Dewi Hanggraeni

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.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Diana López Avilés ◽  
Paula Piñeira ◽  
Víctor Andrés Roco Cáceres ◽  
Felipe Vergara ◽  
Nicolas Araya

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.


2018 ◽  
Vol 12 (1) ◽  
pp. 35 ◽  
Author(s):  
Annalisa Di Clemente

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.


2014 ◽  
Vol 14 (2) ◽  
pp. 114-124 ◽  
Author(s):  
Renata Karkowska

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.


2020 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Rihana Sofie Nabella ◽  
Ghozali Maski ◽  
Setyo Tri Wahyudi

Islamic banking in Indonesia has developed as indicated marked by the establishment of Bank Muamalat Indonesia as the first Islamic bank in Indonesia. Islamic banks—Besides the conventional bansk— are an alternative source of financing which are expected to support the country's economic growth. Banks are also known as risk-prone institutions, one of which is systemic risk. This study aims to measure systemic risk and financial linkages in Islamic commercial banks in Indonesia. This study uses the Conditional Value at Risk (CoVaR) model developed by Adrian and Brunnermeier (2009) with data samples of 8 Islamic banks in Indonesia from January 2012 to December 2018. The results isobtained are the contribution of systemic risk is not determined by the size of bank assets and individual risk. k, sBo that both small banks and large banks can threaten financial system stability. So that it can be a reference for regulators to always supervise all banks, not only large banks but also small banks that have high individual risks.


2020 ◽  
Vol 13 (11) ◽  
pp. 270
Author(s):  
Rui Ding ◽  
Stan Uryasev

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.


Author(s):  
Sheri Markose ◽  
Simone Giansante ◽  
Nicolas A. Eterovic ◽  
Mateusz Gatkowski

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.


2016 ◽  
Vol 5 (1) ◽  
pp. 113-140 ◽  
Author(s):  
Mirna Dumičić

Abstract This paper considers financial stability through the processes of accumulation and materialisation of systemic risks. To this end, the method of principal component analysis on the example of Croatia has been used to construct two composite indicators – a systemic risk accumulation index and an index reflecting the consequences of systemic risk materialisation. In the construction of the indices, the features and risks specific to small open economies were considered. Such an approach to systemic risk analysis facilitates the monitoring and understanding of the degree of financial stability and communication of macroprudential policy makers with the public.


Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos

In this paper, we examine how value at risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR 2010) to the main indices of the 12 stock markets of the countries that have used the euro as their official currency since its initial circulation. We show that gaps in the legislative framework give incentives to investment funds to adopt conventional models for the VaR estimation in order to avoid the increased costs that the advanced models involve. For this reason, we apply the commonly used historical simulation VaR (HVaR) model, which is: (i) taught at most finance classes; (ii) widely applied in the financial industry; and (iii) accepted by CESR (2010). The empirical evidence shows the HVaR does not really contribute to financial stability, and the legislative framework does not offer the appropriate guidance. The HVaR model is not representative of the real financial risk, and does not give any signal for trends in the near future. The HVaR is absolutely backward-looking and this increases the stock market's overreaction. The fact that the suggested confidence level in CESR (2010) is set at 99 percent leads to hidden pro-cyclicality. Scholars and researchers should focus on issues such as the abovementioned, otherwise the VaR estimations will become, sooner or later, just a formality, and such conventional statistical measures rarely contribute to financial stability.


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