The financial crisis has demonstrated that systemic risk due to the interconnectedness of financial-market participants - such as financial institutions, insurers, governments and, even, regulators themselves -can dramatically amplify the consequences of isolated shocks to financial systems and pose a serious threat to prosperity and social stability.
The traditional approach to risk control in financial mathematics is to apply risk measures to single institutions. However, this strategy fails to capture systemic risk because it treats institutions as if they were in isolation, and recent literature in financial mathematics has started to develop various approaches to rectify this deficiency.
In this presentation we will axiomatically introduce and characterize risk-consistent conditional systemic risk measures. This class of conditional systemic risk measures is defined on multidimensional risks and consists of those conditional systemic risk measures which can be decomposed into a state-wise conditional aggregation and a
univariate conditional risk measure. Our studies are based on the axiomatic characterization in [Chen et al., 2013] of a similar class of systemic risk measures in a finite state and unconditional framework. We argue in favor of a conditional framework on general probability spaces for assessing systemic risk. Moreover, we allow for very general aggregation rules, a less restrictive axiomatic setting, and thus a more exible structure which covers many prominent examples of systemic risk measures from the literature and used in practice. Further, we will see how this type of systemic risk measures naturally arises when considering families of consistent (in the sense of strong time consistency) conditional systemic risk measures and discuss some examples.
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