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Modeling study demonstrates regime-conditional clustering of central counterparty clearing members, highlighting state-dependent financial risk differentiation.
Existing central counterparty risk models treat clearing members as homogeneous and assume static risk parameters, ignoring the regime-dependent nature of member risk profiles. This paper proposes NLM-CD (neighbourhood linkage method – conditional distance), a regime-conditional agglomerative clustering algorithm that classifies clearing members by risk profile across distinct market states.
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Gopinathan et al. (2026) studied this question.
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