Prévision des taux de change: Modèles ARIMA, XGBoost, LSTM et Monte Carlo
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Le processus de conversion d'une devise en une autre à des fins diverses - le plus souvent le commerce, le tourisme ou les échanges commerciaux - est connu sous le nom de change, ou forex (FX). Les devises sont négociées l'une par rapport à l'autre sous la forme de paires de taux de change. Par exemple, la paire de devises EUR/USD permet aux traders d'échanger l'euro contre le dollar américain, tandis que la paire GBP/JPY (livre sterling/yen japonais) permet aux traders d'échanger l'euro contre le dollar américain, tandis que la paire GBP/JPY permet aux traders d'échanger l'euro contre le dollar américain. Les marchés des changes (Forex), qui constituent la plus grande arène financière au monde, exigent des stratégies de prévision solides pour s'adapter à leur nature dynamique et complexe. Cette recherche entreprend une analyse comparative approfondie des modèles de prévision couvrant deux décennies, de 2000 à 2019, en utilisant les données de la série chronologique de la Réserve fédérale. Le projet se penche sur le coeur de la prévision des taux de change, en abordant le besoin critique de précision dans la prévision des mouvements des taux de change. Dans ce contexte, la recherche examine l'efficacité de divers modèles, y compris la moyenne mobile intégrée autorégressive (ARIMA) traditionnelle, le XGBoost de l'apprentissage automatique, la mémoire à long terme à court terme (LSTM) de l'apprentissage profond, et la perspective unique offerte par les simulations de Monte Carlo.
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