Learning Forecast-Efficient Yield Curve Factor Decompositions with Neural Networks

Econometrics

Julio M. Stern¹  Ana T. Terada¹  Piero C. Kauffmann¹  Hellinton H. Takada² 

[1] Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo 05508-090, Brazil;Santander Asset Management, Sao Paulo 04543-011, Brazil;

关键词: yield curve forecasting;    neural networks;    machine learning;    bayesian modeling;    yield curve decomposition;    dynamic factor models;   

DOI  :  10.3390/econometrics10020015

来源: DOAJ

【 摘 要 】

Most factor-based forecasting models for the term structure of interest rates depend on a fixed number of factor loading functions that have to be specified in advance. In this study, we relax this assumption by building a yield curve forecasting model that learns new factor decompositions directly from data for an arbitrary number of factors, combining a Gaussian linear state-space model with a neural network that generates smooth yield curve factor loadings. In order to control the model complexity, we define prior distributions with a shrinkage effect over the model parameters, and we present how to obtain computationally efficient maximum a posteriori numerical estimates using the Kalman filter and automatic differentiation. An evaluation of the model’s performance on 14 years of historical data of the Brazilian yield curve shows that the proposed technique was able to obtain better overall out-of-sample forecasts than traditional approaches, such as the dynamic Nelson and Siegel model and its extensions.

【 授权许可】

Unknown