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Home > Business and Economics > Finance and accounting > Finance and the finance industry > Banking > Previsione dei tassi di cambio: Modelli ARIMA, XGBoost, LSTM e Monte Carlo
Previsione dei tassi di cambio: Modelli ARIMA, XGBoost, LSTM e Monte Carlo

Previsione dei tassi di cambio: Modelli ARIMA, XGBoost, LSTM e Monte Carlo


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About the Book

Il processo di conversione di una valuta in un'altra per una serie di scopi - più comunemente il commercio, il turismo o l'attività commerciale - è noto come foreign exchange, o forex (FX). Come coppie di tassi di cambio, le valute vengono scambiate l'una contro l'altra. Ad esempio, la coppia di valute EUR/USD consente ai trader di scambiare l'euro con il dollaro USA, mentre GBP/JPY (sterlina britannica/yen giapponese). I mercati dei cambi (Forex), essendo la più grande arena finanziaria del mondo, richiedono solide strategie di previsione per navigare nella loro natura dinamica e complessa. Questa ricerca intraprende un'analisi comparativa approfondita dei modelli di previsione che abbracciano due decenni, dal 2000 al 2019, utilizzando i dati delle serie temporali della Federal Reserve. Il progetto si addentra nel cuore della previsione dei tassi di cambio, affrontando il bisogno critico di accuratezza nella previsione dei movimenti dei tassi di cambio. In questo contesto, la ricerca esamina l'efficacia di diversi modelli, tra cui il tradizionale AutoRegressive Integrated Moving Average (ARIMA), il machine learning XGBoost, il deep learning Long Short-Term Memory (LSTM) e la prospettiva unica offerta dalle simulazioni Monte Carlo.


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Product Details
  • ISBN-13: 9786208634612
  • Publisher: Edizioni Sapienza
  • Publisher Imprint: Edizioni Sapienza
  • Height: 229 mm
  • No of Pages: 52
  • Returnable: N
  • Sub Title: Modelli ARIMA, XGBoost, LSTM e Monte Carlo
  • Width: 152 mm
  • ISBN-10: 620863461X
  • Publisher Date: 06 Feb 2025
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Spine Width: 3 mm
  • Weight: 136 gr


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Previsione dei tassi di cambio: Modelli ARIMA, XGBoost, LSTM e Monte Carlo
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