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A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements

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  • Bernardina Algieri

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    Department of Economic and Technological Change, Zentrum für Entwicklungsforschung (ZEF), Universität Bonn, Walter-Flex-Straße 3, 53113 Bonn, Germany
    These authors contributed equally to this work.)

  • Arturo Leccadito

    (Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
    These authors contributed equally to this work.)

  • Pietro Toscano

    (Wellington Management Company LLP, 280 Congress Street, Boston, MA 02210, USA
    These authors contributed equally to this work.)

Abstract

This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window.

Suggested Citation

  • Bernardina Algieri & Arturo Leccadito & Pietro Toscano, 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:22-354:d:555557
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