Forecasting Financial Volatility Under Structural Breaks: A Comparative Study of GARCH Models and Deep Learning Techniques

Víctor Chung, Jenny Espinoza () and Renán Quispe
Additional contact information
Víctor Chung: Departamento de Estadística, Universidad Nacional Pedro Ruiz Gallo, Chiclayo 14001, Peru
Jenny Espinoza: Departamento de Ciencia, Universidad Tecnológica del Perú, Chiclayo 14001, Peru
Renán Quispe: Departamento de Incorporación de Nuevos Saberes, Universidad Nacional de Ingeniería, Lima 150101, Peru

JRFM, 2025, vol. 18, issue 9, 1-21

Abstract: The main objective of this study is to evaluate the predictive performance of traditional econometric models and deep learning techniques in forecasting financial volatility under structural breaks. Using daily data from four Latin American stock market indices between 2000 and 2024, we compare GARCH models with neural networks such as LSTM and CNN. Structural breaks are identified through a modified ICSS algorithm and incorporated into the GARCH framework via regime segmentation. The results show that neglecting breaks overstates volatility persistence and weakens predictive accuracy, while accounting for them improves GARCH forecasts only in specific cases. By contrast, deep learning models consistently outperform GARCH alternatives at medium- and long-term horizons, capturing nonlinear and time-varying dynamics more effectively. This study contributes to the literature by bridging econometric and deep learning approaches and offers practical insights for policymakers and investors in emerging markets facing recurrent structural instability.

Keywords: financial volatility; structural breaks; GARCH models; deep learning; LSTM (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/18/9/494/pdf (application/pdf)
https://www.mdpi.com/1911-8074/18/9/494/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:18:y:2025:i:9:p:494-:d:1742074

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().