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Advances in Asset Liability Management and Interest Rate Risk Modeling in Retail and Investment Banking
Subject area: Management and Commerce · Area of research: Asset Liability Management
Abstract
This critical review examines how retail and investment banks measure, model, and govern interest-rate risk within an integrated asset-liability management (ALM) framework. Drawing on a systematically screened corpus of 250 peer-reviewed, DOI-verified journal articles published through 2023, the review synthesises theory, measurement practice, regulatory constraint, and reported performance effects across five themes: the historical evolution of ALM in retail and investment banking, the progression of interest-rate risk modelling from gap and duration analysis to stochastic, dynamic-programming, and machine-learning methods, the reshaping of ALM practice by Basel III capital and liquidity standards, the influence of macroeconomic volatility and geopolitical shocks on model performance, and the integration of ALM with enterprise-wide risk systems. Rather than treating these as separate literatures, the review compares methods, contexts, and findings across studies to identify where evidence converges, where it conflicts, and where it remains thin, most notably in the direct application of machine learning to interest-rate risk in the banking book. The paper contributes an integrative framework linking measurement choice, duration and gap analysis, option-adjusted and stochastic term-structure models, and behavioural assumptions for non-maturity deposits, to governance design, scenario construction, and earnings and economic-value outcomes, and it develops a targeted research agenda for strengthening validation, transparency, and cross-institutional comparison in this area of banking risk management.
Keywords
Asset Liability Management, Interest Rate Risk, Retail Banking, Investment Banking, Risk Modeling, Stochastic Modeling
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How to cite this paper
@article{1723268,
author = {Kofi Okyere Mensah, Ann Kinyanjui, Ifeoma Okoli, Priscilla Agboada, Omotayo Pedro},
title = {Advances in Asset Liability Management and Interest Rate Risk Modeling in Retail and Investment Banking},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {9},
pages = {583-618},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723268.pdf},
abstract = {This critical review examines how retail and investment banks measure, model, and govern interest-rate risk within an integrated asset-liability management (ALM) framework. Drawing on a systematically screened corpus of 250 peer-reviewed, DOI-verified journal articles published through 2023, the review synthesises theory, measurement practice, regulatory constraint, and reported performance effects across five themes: the historical evolution of ALM in retail and investment banking, the progression of interest-rate risk modelling from gap and duration analysis to stochastic, dynamic-programming, and machine-learning methods, the reshaping of ALM practice by Basel III capital and liquidity standards, the influence of macroeconomic volatility and geopolitical shocks on model performance, and the integration of ALM with enterprise-wide risk systems. Rather than treating these as separate literatures, the review compares methods, contexts, and findings across studies to identify where evidence converges, where it conflicts, and where it remains thin, most notably in the direct application of machine learning to interest-rate risk in the banking book. The paper contributes an integrative framework linking measurement choice, duration and gap analysis, option-adjusted and stochastic term-structure models, and behavioural assumptions for non-maturity deposits, to governance design, scenario construction, and earnings and economic-value outcomes, and it develops a targeted research agenda for strengthening validation, transparency, and cross-institutional comparison in this area of banking risk management.},
keywords = {Asset Liability Management, Interest Rate Risk, Retail Banking, Investment Banking, Risk Modeling, Stochastic Modeling},
month = {March},
doi = {https://doi.org/10.64388/IREV6I9-1723268}
}