Forecasting Forex Rates: ARIMA, XGBoost, LSTM, and Monte Carlo Models

The process of converting one currency into another for a variety of purposes—most commonly trade, tourism, or commerce—is known as foreign exchange, or forex (FX). As exchange rate pairs, currencies are traded against one another. For instance, the currency pair EUR/USD allows traders to trade the euro versus the US dollar, while GBP/JPY (British Pound/Japanese Yen). Foreign exchange (Forex) markets, as the world's largest financial arena, demand robust forecasting strategies to navigate their dynamic and complex nature. This research undertakes a thorough comparative analysis of forecasting models spanning two decades, from 2000 to 2019, utilizing data from the Federal Reserve's time series. The project delves into the core of Forex rate forecasting, addressing the critical need for accuracy in predicting exchange rate movements. In this context, the research scrutinizes the efficacy of diverse models, including traditional AutoRegressive Integrated Moving Average (ARIMA), machine learning's XGBoost, deep learning's Long Short-Term Memory (LSTM), and the unique perspective offered by Monte Carlo simulations.