Examinando por Materia "Derivados financieras"

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    Predicción del precio del oro en el mercado spot y el tipo de cambio USD–COP para la optimización del rango de cobertura en derivados de las compañías exportadoras del sector minero
    (Universidad EAFIT, 2024) Gallego Panesso, Cristian Alexander; Almonacid Hurtado, Paula María
    This study addresses the implementation of various time series regression and machine learning models, such as: ARIMA, ARIMAX, SARIMA and Random Forests with the objective of accurately predicting the price of gold in the spot market and the USD–COP exchange rate. Precision in these predictions is crucial for export companies in the mining sector, as it allows them to establish optimal coverage ranges in the use of financial derivatives. Throughout the study, different machine learning algorithms were evaluated and compared, selecting those that provided the most accurate and consistent results. The findings offer a valuable tool for financial risk management and strategic decision making in the context of gold price volatility and exchange rate fluctuations. At the end of the study, it is indicated that the ARIMAX Rolling Forecast model applied in a parameterization (1,1,0) was the most accurate and consistent model over time for the price forecasts of both assets.