Multiple yield curve modeling and forecasting using deep learning

This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts. By combini...

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Bibliographic Details
Published in:ASTIN Bulletin : The Journal of the IAA Vol. 54; no. 3; pp. 463 - 494
Main Authors: Richman, Ronald, Scognamiglio, Salvatore
Format: Journal Article
Language:English
Published: New York, USA Cambridge University Press 01.09.2024
Subjects:
Asset liability management
Deep learning
Discount rates
Economic forecasting
Effectiveness
Expected values
Interest rates
Liability
Life insurance
Machine learning
Securities markets
Solvency
Valuation
Yield curve
Deep learning
transfer learning
asset–liability management
interest rate risk
Real-world modeling
Solvency II, IFRS 17
multiple yield curve modeling
attention models
Nelson–Siegel model
value-at-risk
ISSN:0515-0361, 1783-1350
Online Access:Get full text
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Summary:This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yield curves induced by the globalization of financial markets and exploit it to produce more accurate forecasts. By combining the self-attention mechanism and nonparametric quantile regression, our model generates both point and interval forecasts of future yields. The architecture is designed to avoid quantile crossing issues affecting multiple quantile regression models. Numerical experiments conducted on two different datasets confirm the effectiveness of our approach. Finally, we explore potential extensions and enhancements by incorporating deep ensemble methods and transfer learning mechanisms.
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ISSN:0515-0361
1783-1350
DOI:10.1017/asb.2024.26