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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | FinTech |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2674-1032/4/2/22 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849472210242109440 |
|---|---|
| author | Elysee Nsengiyumva Joseph K. Mung’atu Charles Ruranga |
| author_facet | Elysee Nsengiyumva Joseph K. Mung’atu Charles Ruranga |
| author_sort | Elysee Nsengiyumva |
| collection | DOAJ |
| description | 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 volatility clustering and temporal dependencies in daily exchange rate returns. Using daily data on USD, EUR, and GBP from 2012 to 2024, we evaluate the model’s performance relative to standalone GARCH(1,1) and LSTM models. Empirical results show that the hybrid model improves VaR estimation accuracy by up to 10%, especially during periods of elevated market volatility. These improvements are validated through MSE, MAE, and backtesting statistics. The enhanced accuracy is particularly relevant in emerging markets, where exchange rate dynamics are highly nonlinear and sensitive to external shocks. The proposed approach offers practical insights for financial institutions and regulators seeking to improve market risk assessment in emerging economies. |
| format | Article |
| id | doaj-art-96b652ed5d914cf7a0afd03af82e2f06 |
| institution | Kabale University |
| issn | 2674-1032 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | FinTech |
| spelling | doaj-art-96b652ed5d914cf7a0afd03af82e2f062025-08-20T03:24:36ZengMDPI AGFinTech2674-10322025-06-01422210.3390/fintech4020022Hybrid GARCH-LSTM Forecasting for Foreign Exchange RiskElysee Nsengiyumva0Joseph K. Mung’atu1Charles Ruranga2African Centre of Excellence in Data Science, University of Rwanda, Kigali P.O. Box 428, RwandaDepartment of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, KenyaAfrican Centre of Excellence in Data Science, University of Rwanda, Kigali P.O. Box 428, RwandaThis 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 volatility clustering and temporal dependencies in daily exchange rate returns. Using daily data on USD, EUR, and GBP from 2012 to 2024, we evaluate the model’s performance relative to standalone GARCH(1,1) and LSTM models. Empirical results show that the hybrid model improves VaR estimation accuracy by up to 10%, especially during periods of elevated market volatility. These improvements are validated through MSE, MAE, and backtesting statistics. The enhanced accuracy is particularly relevant in emerging markets, where exchange rate dynamics are highly nonlinear and sensitive to external shocks. The proposed approach offers practical insights for financial institutions and regulators seeking to improve market risk assessment in emerging economies.https://www.mdpi.com/2674-1032/4/2/22hybrid GARCH-LSTM modeldeep learning in financeforeign exchange riskvalue at risk (VaR)emerging market forecasting |
| spellingShingle | Elysee Nsengiyumva Joseph K. Mung’atu Charles Ruranga Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk FinTech hybrid GARCH-LSTM model deep learning in finance foreign exchange risk value at risk (VaR) emerging market forecasting |
| title | Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk |
| title_full | Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk |
| title_fullStr | Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk |
| title_full_unstemmed | Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk |
| title_short | Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk |
| title_sort | hybrid garch lstm forecasting for foreign exchange risk |
| topic | hybrid GARCH-LSTM model deep learning in finance foreign exchange risk value at risk (VaR) emerging market forecasting |
| url | https://www.mdpi.com/2674-1032/4/2/22 |
| work_keys_str_mv | AT elyseensengiyumva hybridgarchlstmforecastingforforeignexchangerisk AT josephkmungatu hybridgarchlstmforecastingforforeignexchangerisk AT charlesruranga hybridgarchlstmforecastingforforeignexchangerisk |