Forecasting exchange rates with elliptically symmetric principal components

Karo Solat and Kwok Ping Tsang

International Journal of Forecasting, 2021, vol. 37, issue 3, 1085-1091

Abstract: We extract elliptically symmetric principal components from a panel of 17 OECD exchange rates and use the deviations from the components to forecast future exchange rate movements, following the method in Engel et al. (2015). Instead of using standard factor models, we apply elliptically symmetric principal component analysis (ESPCA), introduced by Solat and Spanos (2018), which captures both contemporaneous and temporal co-variation among the exchange rates. We find that ESPCA is more accurate than forecasts generated by existing standard methods and the random walk model, with or without including macroeconomic fundamentals.

Keywords: Factor model; Principal component analysis; Exchange rates; Out-of-sample forecasting; Elliptically symmetric principal components (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:3:p:1085-1091

DOI: 10.1016/j.ijforecast.2020.11.007

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