Modeling the yield curve of BRICS countries: Parametric vs. machine learning techniques

Artikel

We compare parametric and machine learning techniques (namely: Neural Networks) for in-sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende-Ferreira five-factor model with time-varying decay parameters and a Feed-Forward Neural Network to the bond market data of the BRICS countries. To enhance the flexibility of the parametric model, we also introduce a new procedure to estimate the time varying parameters that significantly improve its performance. Our contribution spans towards two directions. First, we offer a comprehensive investigation of the bond market in the BRICS countries examined both by time and maturity; working on five countries at once we also ensure that our results are not specific to a particular data-set; second we make recommendations concerning modelling and estimation choices of the yield curve. In this respect, although comparing highly flexible estimation methods, we highlight superior in-sample capabilities of the neural network in all the examined markets and then suggest that machine learning techniques can be a valid alternative to more traditional methods also in presence of marked turbulence.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Risks ; ISSN: 2227-9091 ; Volume: 10 ; Year: 2022 ; Issue: 2 ; Pages: 1-18 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: Artificial Neural Network (ANN) ; BRICS ; De Rezende-Ferreira model ; emerging markets ; Feed-Forward Neural Network (FFNN) ; term structure

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Castello, Oleksandr ; Resta, Marina
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2022

Further information

• DOI: doi:10.3390/risks10020036
• Handle: http://hdl.handle.net/10419/258347
• Last update: 10.03.2025, 11:42 AM CET