Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

Sylwester Bejger; Piotr Fiszeder

doi:10.5604/01.3001.0015.5582

Sylwester Bejger Nicolaus Copernicus University in Toruń, Faculty of Economic Sciences and Management, Department of Applied Informatics and Mathematics in Economics, ul. Gagarina 13a, 87-100 Toruń, Poland ORCID:https://orcid.org/0000-0001-7900-946X , Piotr Fiszeder Nicolaus Copernicus University in Toruń, Faculty of Economic Sciences and Management, Department of Econometrics and Statistics, ul. Gagarina 13a, 87-100 Toruń, Poland ORCID:https://orcid.org/0000-0002-9777-2239 Przegląd Statystyczny. Statistical Review, vol. 68, 2021, 3, pages: 1-15 Published online: 30 December 2021 DOI 10.5604/01.3001.0015.5582

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ARTICLE

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ABSTRACT

We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method.

KEYWORDS

machine learning, tree-based ensembles, volatility models, high-low range, covariance forecasting

JEL

C22, C45, C53, C58, C63, G17

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