Analysis of credit-rating migrations with genetic algorithms. (12th January 2021)

• Record Type: Journal Article
• Authors: Kaniovski, Yuri; Kaniovskyi, Yuriy; Pflug, Georg
• Is Part Of: International journal of bio-inspired computation. Volume 16:Number 4(2020)
• Page Start: 264
• Page End: 274
• Publication Date: 2021-01-12
• Subjects: heuristics -- encoding -- nonlinear programming -- mutation -- parallel -- sequential -- maximum likelihood -- selection -- threshold -- random search; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

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.