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Hybrid Modelling of Exchange Rate Dynamics: Policy Insights from USD-INR Forecasting with Machine Learning and Econometrics
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Abstract
En 中文
This study examines the dynamics of India's economy over the past six and a half decades (1960-2024), with a focus on how the USD-INR exchange rate interacts with key factors, including the Consumer Price Index (CPI), lending rates, and the GBP-USD currency rate. To ensure the data was ready for analysis, we applied a range of pre-processing techniques, including log transformations, differencing, and normalisation, followed by stationarity checks using both the Augmented Dickey-Fuller (ADF) and KPSS tests. Authors have compared traditional econometric models such as ARIMA & GARCH with deep learning approaches like LSTM, XGBoost, and LightGBM. To evaluate performance fairly, we used rigorous validation techniques such as walk-forward validation and time-series cross-validation, along with interpretability tools like SHAP, partial dependence plots (PDP), and LIME. The results show that LSTM models significantly outperformed conventional methods, reducing error by 35% (based on RMSE). Among all predictors, CPI proved to be the most influential driver of USD-INR movements. Importantly, the models remained reliable even during periods of major economic turbulence, achieving an out-of-sample R-2 of 0.783.
Keywords:
Machine Learning
Time series
ARIMA
LSTM
Hybrid model
GPR
Foreign Exchange
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0.1
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42
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