We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to a common FMI participant. We construct indicators of credit risk exposures in three main Canadian FMIs and use multivariate extreme value methods to estimate this probability. We find large differences in the levels of systemic risk across participants. Conditional on the participant being distressed, we re-estimate these probabilities and find that some participants create large exposures to FMIs, resulting in a larger level of systemic risk than the rest of the participants. Our results suggest that an appropriate oversight of FMIs may benefit from an in-depth system-wide analysis, which may have useful implications for the macroprudential regulation of the financial system.

This paper proposes a set of market-based measures on the systemic importance of a financial institution or a group of financial institutions, each designed to capture different aspects of systemic importance of financial institutions. Multivariate extreme value theory approach is used to estimate these measures. Using six big Canadian banks as the proxy for Canadian banking sector, we apply these measures to identify systemically important banks in Canadian banking sector and major risk contributors from international financial institutions to Canadian banking sector. The empirical evidence reveals that (i) the top three banks, RBC Financial Group, TD Bank Financial Group, and Scotiabank, are more systemically important than other banks, while we also find that the size of a financial institution should not be considered as a proxy of systemic importance; (ii) compared to the European and Asian banks, the crashes of the U.S. banks, on average, are the most damaging to Canadian banking sector, while the risk contribution to the Canadian banking sector from Asian banks is quite lower than that from banks in the U.S. and euro area; (iii) the risk contribution to Canadian banking sector exhibits “home bias”, that is, cross-country risk contribution tends to be smaller than domestic risk contribution.

The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky et al. (1998) is used to monitor the evolution of a number of economic indicators that tend to exhibit unusual behavior in the periods preceding a financial stress event. Based on the individual indicators from 13 OECD countries, we propose three different composite indicators, the summed composite indicator, the extreme composite indicator and the weighted composite indicator. The in-sample forecasting results for the 13 OECD countries indicate that the three composite indicators are useful tools for predicting financial stress events, while none of them outperforms the others across all the criteria considered. The out-of-sample forecasting results suggest that for most of the 13 OECD countries, including Canada, the United Kingdom and the United States, the weighted composite indicator performs better than the two others across all the criteria considered.