Sylwester Bejger https://orcid.org/0000-0001-7900-946X , Piotr Fiszeder https://orcid.org/0000-0002-9777-2239
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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