Measuring Systemic Risk in the Korean Banking Sector via Dynamic Conditional Correlation Models

Yun, Jaeho; Moon, Hyejung

doi:10.2139/ssrn.2580592

Cited by 5 publications

(7 citation statements)

References 20 publications

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“…We also find that the similarities and the differences of our measures of systemic risk are country‐varying. For instance, the rankings based on ΔCoVaR and MES have no significant correlation for Chinese banks while Yun and Moon () find that they are highly correlated for Korean banks. Overall, our findings have the important policy implication that financial regulators should acknowledge the different meaning of different systemic risk measures, and that they should not rely on one single measure to identify systemic risk of banks.…”

Section: Results and Analysismentioning

confidence: 99%

“…Notes: ΔCoVaR (MES) in Yun and Moon () are the mean ΔCoVaR (MES) of 10 banks in Korea during 2008–2013. ΔCoVaR (MES) in this paper reported here are the mean ΔCoVaR (MES) of 16 banks in China during 2008–2013.…”

Section: Results and Analysismentioning

confidence: 99%

“…In this section, we compare our results of ΔCoVaR and MES for China with those for Korea presented by Yun and Moon (2014). 18 The results shown in Table 12 for China and Korea refer to the same period, 2008-2013.…”

Section: Comparison With Korean Banksmentioning

confidence: 99%

See 2 more Smart Citations

Analysing Systemic Risk in the Chinese Banking System

Huang

Haan

Scholtens

2017

Pacific Economic Review

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We examine systemic risk in the Chinese banking system by estimating the conditional value at risk (CoVaR), the marginal expected shortfall (MES), the systemic impact index (SII) and the vulnerability index (VI) for 16 listed banks in China for the 2007–2014 period. We find that these measures show different patterns, capturing different aspects of systemic risk of Chinese banks. However, rankings of banks based on these measures are significantly correlated. The time‐series results for the CoVaR and MES measures suggest that systemic risk in the Chinese banking system decreased after the global financial crisis but started rising in 2014.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

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Analysing Systemic Risk in the Chinese Banking System

Huang

Haan

Scholtens

2017

Pacific Economic Review

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“…Temuan ini sejalan dengan teori bahwa Bank dengan permodalan yang kuat akan dapat bertahan. Penelitian sebelumnya jika menemukan bahwa banyak Bank dengan modal yang besar akan dapat memberikan kontribusi yang lebih besar terhadap kejadian sistemik jika banyak Bank tersebut mengalami kondisi default (Yun and Moon, 2014;Jonghe et. al., 2015;Pangestuti, 2018).…”

Section: Hasilunclassified

Evaluasi Kinerja Perbankan ASEAN Terkait Peningkatan Risiko Sistemik dalam Integrasi Pasar Perdagangan Bebas Masyarakat Ekonomi ASEAN (MEA)

Pangestuti

2020

Ekonomis

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This study is aimed to provide an evaluation of banking performance in ASEAN given the inclusion of the ASEAN Economic Community (AEC) which is associated with an increase in potential systemic risk. Some of the main findings in this study are: (a) the increase in systemic risk is influenced by the increase in risk on asset quality as seen from the increase in the percentage of non-performing loans; (b) bank liquidity has a significant positive effect on increasing systemic risk; (c) banks with large capital can exert influence over increased systemic risk (in a 90% confidence level but here we use 95% confidence level). The government is expected to be able to mitigate the increase in potential systemic risks, especially after the integration of free trade in ASEAN.

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“…Using the CoVaR and Granger causality network, they investigated the effects of interconnectedness on the systemic risk in the Asian financial market. In another paper, Yun and Moon used the dynamic conditional correlation model to estimate both measures of CoVaR and SRISK in the South Korean banking system (Yun & Moon, 2014). In another study, the ΔCoVaR used as a measure to compare the roles of foreign and domestic banks in the systemic crises of the United States and the United Kingdom.…”

Section: Introductionmentioning

confidence: 99%

Network‐based early warning system to predict financial crisis

Dastkhan

2019

Int. J Fin Econ

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In recent years, early warning systems have been developed as a significant tool to prevent financial and economic crises. In this paper, we applied the forward‐looking conditional value at risk (COVaR) as a market‐based systemic risk measure to analyse the systemic risk in an emerging market. A network representation of asset exposures is presented based on the values of ΔCoVaR. According to the exposure networks, two new network‐based indices are introduced. Furthermore, regarding the dependence of the new indices on the values of forward‐looking CoVaR, they are applied as the indicators for the proposed early warning system to predict downturns and crises in the level of firms and the whole market. The results of the early warning system for the market return show that the indicators based on both proposed models have a good ability to predict the crises in the market. In other words, the indicators issued a warning signal up to seven periods before the real crisis in the market. Similarly, the results of the early warning system to predict downturns at the level of firms also indicate the ability of indicators to issue the true warning signals. The results of the proposed system can help policymakers and authorities to determine the appropriate microprudential and macroprudential policies to avoid systemic risks.

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Measuring Systemic Risk in the Korean Banking Sector via Dynamic Conditional Correlation Models

Cited by 5 publications

References 20 publications

Analysing Systemic Risk in the Chinese Banking System

Analysing Systemic Risk in the Chinese Banking System

Evaluasi Kinerja Perbankan ASEAN Terkait Peningkatan Risiko Sistemik dalam Integrasi Pasar Perdagangan Bebas Masyarakat Ekonomi ASEAN (MEA)

Network‐based early warning system to predict financial crisis

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