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Aggregating Expert Knowledge for the Measurement of Systemic Risk

Mezei József, Sarlin Peter, Aggregating Expert Knowledge for the Measurement of Systemic Risk. Decision Support Systems 88, 38–50, 2016.



The policy objective of safeguarding financial stability has stimulated a wave of research on systemic risk analytics, yet it still faces challenges in measurability. This paper models systemic risk by tapping into expert knowledge of financial supervisors. We decompose systemic risk into a number of interconnected segments, for which the level of vulnerability is measured. The system is modeled in the form of a Fuzzy Cognitive Map (FCM), in which nodes represent vulnerability in segments and links their interconnectedness. A main problem tackled in this paper is the aggregation of values in different interrelated nodes of the map to obtain an estimate of systemic risk. To this end, Choquet integral-based aggregation is employed to expert evaluations of measures, as it allows for the integration of interrelations among factors in the aggregation process. The approach is illustrated through two applications in a European setting. First, we provide an estimation of systemic risk with a Pan-European set-up. Second, we estimate country-level risks, allowing for a more granular decomposition. This sets a starting point for the use of the rich, oftentimes tacit, knowledge in policy organizations. (C) 2016 Elsevier B.V. All rights reserved.

BibTeX entry:

  title = {Aggregating Expert Knowledge for the Measurement of Systemic Risk},
  author = {József, Mezei and Peter, Sarlin},
  journal = {Decision Support Systems},
  volume = {88},
  publisher = {Elsevier},
  pages = {38–50},
  year = {2016},
  keywords = {Systemic risk;Aggregation operators;Fuzzy Cognitive Maps;Choquet integral},
  ISSN = {0167-9236},

Belongs to TUCS Research Unit(s): Institute for Advanced Management Systems Research (IAMSR)

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