Title: Novel variants of the TOPSIS algorithm to select and rate the bank counterparties

Authors: Kala Nisha Gopinathan; Punniyamoorthy Murugesan; Hari Hara Krishna Kumar Viswanathan; Matthew Mitchell

Addresses: Department of Management Studies, National Institute of Technology Tiruchirappalli, 620015, Tamil Nadu, India ' Department of Management Studies, National Institute of Technology Tiruchirappalli, 620015, Tamil Nadu, India ' JPMorgan Services India Private Limited, Tower 8A, Embassy Tech Village Rd., Bellandur, Bengaluru, 560103. Karnataka, India ' JPMorgan Chase, 270 Park Avenue, 10017, New York Office, USA

Abstract: Credit rating agencies (CRAs) assign ratings to banks using the through-the-cycle (TTC) approach, which often fails to reflect the current condition of banks. Selecting bank counterparties is crucial in the derivatives market, with credit ratings typically guiding this choice. This study introduces two innovative variants of the technique for order of preference by similarity to the ideal solution (TOPSIS) for selecting and rating bank counterparties. These variants, TOPSIS1 and TOPSIS2, depart from the traditional TTC approach by using point-in-time analysis. We analyse the TOPSIS scores and rankings using statistical measures like Spearman's rank correlation coefficient. The results show that TOPSIS2 is a practical, interpretable method for rating unrated banks, predicting upgrades/downgrades, and mitigating counterparty credit risk (CCR).

Keywords: OTC derivatives; credit ratings; counterparty risk mitigation strategy; TOPSIS; multi-criteria decision-making; MCDM; credit support annex; CSA.

DOI: 10.1504/IJCEE.2025.147777

International Journal of Computational Economics and Econometrics, 2025 Vol.15 No.3, pp.265 - 293

Received: 12 Oct 2023
Accepted: 29 Jul 2024
Published online: 31 Jul 2025 *

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