Title: A comparative study of machine learning and grey approach for forecasting FX rates

Authors: Noorshanaaz Khodabaccus; Aslam Aly El Faidal Saib

Addresses: Doctoral School, University of Technology, Mauritius ' Department of Applied Mathematical Sciences, School of Innovative Technologies and Engineering, University of Technology, Mauritius

Abstract: Volatility in the foreign exchange (FX) market is often associated with risk and can disrupt the sustainable development of an economy. The Mauritian economy, being open and globally integrated, is highly sensitive to currency fluctuations. Consequently, modelling and forecasting FX market volatility is crucial for proper risk management. This paper presents a comparative study on FX rate modelling and forecasting accuracy, contrasting conventional deep learning approaches with grey models. In particular, we compare the performance of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, using high-frequency historical data against the basic grey model and the optimised Fourier grey Markov (FOGM) model, which relies on a significantly smaller dataset. Our findings indicate that the FOGM model, despite using a smaller dataset, outperforms the deep learning approaches considered.

Keywords: FX rates forecasting; volatility; deep learning; grey model; optimised Fourier grey Markov model.

DOI: 10.1504/IJCAST.2026.155954

International Journal of Complexity in Applied Science and Technology, 2026 Vol.2 No.3, pp.246 - 270

Received: 16 Mar 2025
Accepted: 18 May 2025
Published online: 27 Aug 2026 *

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