Resolutions to flip-over credit risk and beyond

Bill Huajian Yang

MPRA Paper from University Library of Munich, Germany

Abstract: Abstract Given a risk outcome y over a rating system {R_i }_(i=1)^k for a portfolio, we show in this paper that the maximum likelihood estimates with monotonic constraints, when y is binary (the Bernoulli likelihood) or takes values in the interval 0≤y≤1 (the quasi-Bernoulli likelihood), are each given by the average of the observed outcomes for some consecutive rating indexes. These estimates are in average equal to the sample average risk over the portfolio and coincide with the estimates by least squares with the same monotonic constraints. These results are the exact solution of the corresponding constrained optimization. A non-parametric algorithm for the exact solution is proposed. For the least squares estimates, this algorithm is compared with “pool adjacent violators” algorithm for isotonic regression. The proposed approaches provide a resolution to flip-over credit risk and a tool to determine the fair risk scales over a rating system.

Keywords: risk scale; maximum likelihood; least squares; isotonic regression; flip-over credit risk (search for similar items in EconPapers)
JEL-codes: C10 C13 C14 C18 C6 C61 C63 C65 C67 C8 C80 G12 G17 G18 G3 G32 G35 (search for similar items in EconPapers)
Date: 2019-03-18
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
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Published in Big Data and Information Analytics 2.3(2019): pp. 54-67

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:93389

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