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Title: Analysis of credit-rating migrations with genetic algorithms

Authors: Yuri Kaniovski; Yuriy Kaniovskyi; Georg Pflug

Addresses: Faculty of Economics and Management, Free University of Bozen-Bolzano, Bolzano, BZ, Italy ' Research Group Scientific Computing, Faculty of Computer Science, University of Vienna, Vienna, Austria ' Department of Statistics and Operations Research, University of Vienna, Vienna, Austria

Abstract: Modelling dependent credit-rating migrations of assets classified into M credit classes and S industries, M × S + 2^M×S parameters have to be estimated. For a realistic choice of M and S, this number is huge and it greatly exceeds the number of available observations. To avoid brute-force calculations, we suggest sequential and parallel genetic algorithms. Considering a practically important combination of M = 7 and S = 6, the approach is tested on Standard and Poor's data.

Keywords: heuristics; encoding; nonlinear programming; mutation; parallel; sequential; maximum likelihood; selection; threshold; random search.

DOI: 10.1504/IJBIC.2020.112348

International Journal of Bio-Inspired Computation, 2020 Vol.16 No.4, pp.264 - 274

Received: 06 Dec 2019
Accepted: 06 Aug 2020
Published online: 12 Jan 2021 *