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Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models

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  • Yun, Jaeho
  • Moon, Hyejung

Abstract

In this paper we study systemic risks in the Korean banking sector by using two famous systemic risk measures — the MES (marginal expected shortfall) and CoVaR. To compute both measures we employ Engle's dynamic conditional correlation model. Our empirical analysis shows, first, that although these two systemic risk measures differ in defining the contributions to systemic risk, both are qualitatively very similar in explaining the cross-sectional differences in systemic risk contributions across banks. Second, we find that systemic risk contributions are closely related to certain bank characteristic variables (e.g., VaR (value at risk), size and leverage ratio). However, there are differences between the cross-sectional and the time series dimensions in the effects of these variables. Last, using a threshold VAR model, we suggest an overall systemic risk measure – the aggregate MES – and its associated threshold value for use as an early warning indicator.

Suggested Citation

  • Yun, Jaeho & Moon, Hyejung, 2014. "Measuring systemic risk in the Korean banking sector via dynamic conditional correlation models," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 94-114.
    • Handle: RePEc:eee:pacfin:v:27:y:2014:i:c:p:94-114
    DOI: 10.1016/j.pacfin.2014.02.005
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    2. Song, Jae Wook & Ko, Bonggyun & Cho, Poongjin & Chang, Woojin, 2016. "Time-varying causal network of the Korean financial system based on firm-specific risk premiums," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 287-302.
    3. Edward M. H. Lin & Edward W. Sun & Min-Teh Yu, 2018. "Systemic risk, financial markets, and performance of financial institutions," Annals of Operations Research, Springer, vol. 262(2), pages 579-603, March.
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    5. Qiubin Huang & Jakob De Haan & Bert Scholtens, 2019. "Analysing Systemic Risk in the Chinese Banking System," Pacific Economic Review, Wiley Blackwell, vol. 24(2), pages 348-372, May.
    6. Katherine Uylangco & Siqiwen Li, 2016. "An evaluation of the effectiveness of Value-at-Risk (VaR) models for Australian banks under Basel III," Australian Journal of Management, Australian School of Business, vol. 41(4), pages 699-718, November.
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    9. Aymen Mselmi & Imen Mahmoud, 2023. "Systemic Risk: A Comparative Study between Public and Private Banks," International Journal of Economics and Financial Issues, Econjournals, vol. 13(3), pages 117-125, May.
    10. Shahzad, Syed Jawad Hussain & Arreola-Hernandez, Jose & Bekiros, Stelios & Shahbaz, Muhammad & Kayani, Ghulam Mujtaba, 2018. "A systemic risk analysis of Islamic equity markets using vine copula and delta CoVaR modeling," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 104-127.
    11. Rahman, Md Lutfur & Troster, Victor & Uddin, Gazi Salah & Yahya, Muhammad, 2022. "Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience," International Review of Financial Analysis, Elsevier, vol. 79(C).
    12. Jianxu Liu & Quanrui Song & Yang Qi & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions," Sustainability, MDPI, vol. 12(10), pages 1-15, May.
    13. Das, Sanjiv R. & Kalimipalli, Madhu & Nayak, Subhankar, 2022. "Banking networks, systemic risk, and the credit cycle in emerging markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    14. Hossein Dastkhan, 2021. "Network‐based early warning system to predict financial crisis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 594-616, January.
    15. Pham, Thach N. & Powell, Robert & Bannigidadmath, Deepa, 2021. "Systemically important banks in Asian emerging markets: Evidence from four systemic risk measures," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    16. Christian Brownlees & Giuseppe Cavaliere & Alice Monti, 2018. "Evaluating The Accuracy Of Tail Risk Forecasts For Systemic Risk Measurement," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-25, June.
    17. Necmi Kemal Avkiran & Lin Mi, 2017. "The Rising Systemic Importance of Chinese Banks: Should the World Be Concerned?," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(4), pages 427-440, December.
    18. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
    19. Acedański, Jan & Karkowska, Renata, 2022. "Instability spillovers in the banking sector: A spatial econometrics approach," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    20. Fang, Lei & Cheng, Jiang & Su, Fang, 2019. "Interconnectedness and systemic risk: A comparative study based on systemically important regions," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 147-158.

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    More about this item

    Keywords

    Systemic risk; DCC (dynamic conditional correlation) model; MES (marginal expected shortfall); CoVaR; Threshold VAR;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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