Designing an expert knowledge-based Systemic Importance Index for financial institutions
AbstractDefining whether a financial institution is systemically important (or not) is challenging due to (i) the inevitability of combining complex importance criteria such as institutions’ size, connectedness and substitutability; (ii) the ambiguity of what an appropriate threshold for those criteria may be; and (iii) the involvement of expert knowledge as a key input for combining those criteria. The proposed method, a Fuzzy Logic Inference System, uses four key systemic importance indicators that capture institutions’ size, connectedness and substitutability, and a convenient deconstruction of expert knowledge to obtain a Systemic Importance Index. This method allows for combining dissimilar concepts in a non-linear, consistent and intuitive manner, whilst considering them as continuous –non binary- functions. Results reveal that the method imitates the way experts them-selves think about the decision process regarding what a systemically important financial institution is within the financial system under analysis. The Index is a comprehensive relative assessment of each financial institution’s systemic importance. It may serve financial authorities as a quantitative tool for focusing their attention and resources where the severity resulting from an institution failing or near-failing is estimated to be the greatest. It may also serve for enhanced policy-making (e.g. prudential regulation, oversight and supervision) and decision-making (e.g. resolving, restructuring or providing emergency liquidity).
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Bibliographic InfoPaper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 669.
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Systemic Importance; Systemic Risk; Fuzzy Logic; Approximate Reasoning; Too-connected-to-fail; Too-big-to-fail. Classification JEL: D85; C63; E58; G28.;
Other versions of this item:
- Carlos Léon & Clara Machado, 2011. "Designing an expert knowledge-based Systemic Importance Index for financial institutions," BORRADORES DE ECONOMIA 008953, BANCO DE LA REPÚBLICA.
- D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
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- Carlos León & Ron J. Berndsen, 2013.
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