Forecasting Dynamic Term Structure Models with Autoencoders

C Castro-Iragorri () and J Ramírez

No 19431, Documentos de Trabajo from Universidad del Rosario

Abstract: Principal components analysis (PCA) is a statistical approach to build factor models in finance. PCA is also a particular case of a type of neural network known as an autoencoder. Recently, autoencoders have been successfully applied in financial applications using factor models, Gu et al. (2020), Heaton and Polson (2017). We study the relationship between autoencoders and dynamic term structure models; furthermore we propose different approaches for forecasting. We compare the forecasting accuracy of dynamic factor models based on autoencoders, classical models in term structure modelling proposed in Diebold and Li (2006) and neural network-based approaches for time series forecasting. Empirically, we test the forecasting performance of autoencoders using the U.S. yield curve data in the last 35 years. Preliminary results indicate that a hybrid approach using autoencoders and vector autoregressions framed as a dynamic term structure model provides an accurate forecast that is consistent throughout the sample. This hybrid approach overcomes in-sample overfitting and structural changes in the data.

Keywords: autoencoders; factor models; principal components; recurrentneural networks (search for similar items in EconPapers)
JEL-codes: C45 C53 C58 (search for similar items in EconPapers)
Pages: 36
Date: 2021-07-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-for, nep-isf and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:col:000092:019431

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