Algorithmic Strategies for Hedging Interest Rate Risk in The Debt Market

Аннотация

This article examines contemporary OPEN ACCESS algorithmic approaches to multiparametric SUBMITTED 04 June 2025 ACCEPTED 24 June 2025 immunization of interest rate risk in a fixed-income PUBLISHED 22 July 2025 portfolio, explicitly accounting for non-parallel shifts in VOLUME Vol.07 Issue 07 2025 the yield curve. Using the Nelson–Siegel framework, the bond-price sensitivities to the three primary factors— CITATION Elmurat Erkin uulu. (2025). Pricing in the Direct Supply of Construction level, slope, and curvature—are characterized, and the Components. The American Journal of Management and Economics Innovations, 7(07), 96–103. traditional Duration and Duration–Convexity https://doi.org/10.37547/tajmei/Volume07Issue07-11 immunization strategies are reviewed. It is COPYRIGHT demonstrated that attempting to hedge all three factors © 2025 Original content from this work may be used under the terms simultaneously with classical techniques often yields of the creative commons attributes 4.0 License. extreme portfolio weights, excessive leverage, and poor out-of-sample performance. To overcome these limitations, we implement L¹ (Lasso) and L² (Ridge) regularization—subject to a strict overall leverage cap— on U.S. Treasury data. An empirical replication of a “retirement bond” (a pension-payment stream) shows that leverage-constrained Lasso strategies reduce the median absolute deviation of the funding ratio while also lowering turnover. These results confirm the hypothesis that regularization improves both the robustness and economic efficiency of interest-rate hedging for institutional investors with long-dated liabilities. The insights presented will interest financial- engineering researchers specializing in stochastic yield- curve modeling and optimal portfolio-management methods. Portfolio managers, institutional risk officers, and quantitative teams at hedge funds seeking to integrate high-frequency and machine-learning algorithms into their volatility-reduction workflows and to ensure stable returns amid changing market rates will also find practical guidance here. Keywords: algorithmic hedging; interest rate risk; regularization; Lasso; Ridge; immunization; Nelson– Siegel model; pension liabilities The American Journal of Management and Economics Innovations 104 https://www.theamericanjournals.com/index.php/tajmei Introduction Spelta A. [7] describe deploying dynamic algorithms based on gradient boosting and recurrent neural Interest rate risk remains a central challenge for networks to capture nonlinear currency effects when participants in the fixed-income market, particularly hedging interest-rate risk in a global portfolio, reporting institutional investors with long-dated liabilities superior hedge-efficiency metrics versus classical (pension funds, insurance companies). Traditional duration-based approaches. In the broader context of AI immunization strategies based on Duration and integration, Khatri C. A. [4] analyzes the application of Convexity effectively guard a portfolio only against deep-learning and reinforcement-learning methods to parallel shifts of the yield curve. In practice, however, the pricing and hedging of currency and credit non-parallel moves in slope and curvature frequently derivatives. occur, resulting in hedging errors and diminished risk- management effectiveness [1]. The second strand focuses on state-switching stochastic models that reflect alternating market regimes. The objective of this study is to investigate algorithmic, Gubareva M. and Keddad B. [5] apply Markov-switching multiparameter immunization strategies for interest models to emerging-market sovereign debt, enabling rate risk that employ L¹ (Lasso) and L² (Ridge) the hedging system to distinguish between “crisis” and regularization in conjunction with leverage constraints, “calm” regimes in interest-rate dynamics. They thereby optimally balancing the bias–variance trade-off demonstrate that, under heightened volatility, of factor exposures. traditional immunization strategies falter, whereas The scientific novelty lies in demonstrating, for the first switching-state models automatically recalibrate hedge time, that incorporating L¹ and L² regularization with a positions according to the prevailing regime. strict cap on total leverage within algorithmic The third strand addresses the banking sector and multiparameter immunization frameworks produces strategies for hedging margin-rate risk. Cherrat H. and sparse, leverage-controlled portfolios. These portfolios Prigent J. L. [6] concentrate on hedging the interest-rate exhibit markedly lower median and maximum absolute risk inherent in banks’ marginal profitability on demand- deviations in the funding ratio and reduced transaction deposit portfolios. They devise algorithmic strategies turnover compared with conventional Duration and combining interest-rate derivatives—such as swaps and Duration–Convexity methods when the yield curve rate options—with proprietary