Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk

Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk

This study proposes a hybrid forecasting model that integrates the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a Long Short-Term Memory (LSTM) neural network to estimate Value at Risk (VaR) in the Rwandan foreign exchange market. The model is designed to capture both...

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Bibliographic Details
Main Authors: Elysee Nsengiyumva, Joseph K. Mung’atu, Charles Ruranga
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:FinTech
Subjects:
hybrid GARCH-LSTM model
deep learning in finance
foreign exchange risk
value at risk (VaR)
emerging market forecasting
Online Access:https://www.mdpi.com/2674-1032/4/2/22
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