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

• We study systemic risks in the Korean banking sector.
• We estimate and compare the two systemic risk measures.
• The dynamic conditional correlation model is used for estimation.
• We analyze determinants of systemic risk contribution of each bank.
• An overall systemic risk measure is proposed via a threshold VAR model.

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.