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Fuzzy decision fusion approach for loss-given-default modeling

Abdolreza Nazemi, Farnoosh Fatemi Pour, Konstantin Heidenreich and Frank Fabozzi ()

European Journal of Operational Research, 2017, vol. 262, issue 2, 780-791

Abstract: In this paper, fuzzy decision fusion techniques are applied to predict loss-given-default of corporate bonds. In our model, we add the principal components derived from more than 100 macroeconomic variables as explanatory variables. However, in order to improve the performance of the model, the Box–Cox transformation of macroeconomic variables is applied prior to loss-given-default modeling. A differential evolution algorithm is used to create an optimized fuzzy rule-based model that fuses the outputs of several base models. We compare the predictions from fuzzy decision fusion techniques with support vector regression techniques, regression trees and OLS regressions. Our findings show that fuzzy decision fusion techniques increase prediction accuracy of loss-given-default modeling and transformations of macroeconomic factors do not affect prediction accuracy of fuzzy models.

Keywords: Risk management; Loss-given-default modeling; Fuzzy rule-based model; Macroeconomic variables; Corporate bonds (search for similar items in EconPapers)
Date: 2017
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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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Handle: RePEc:eee:ejores:v:262:y:2017:i:2:p:780-791