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Article

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

by 1,2,*,‡, 1,‡ and 3,‡
1
Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
2
Department of Economic and Technological Change, Zentrum für Entwicklungsforschung (ZEF), Universität Bonn, Walter-Flex-Straße 3, 53113 Bonn, Germany
3
Wellington Management Company LLP, 280 Congress Street, Boston, MA 02210, USA
*
Author to whom correspondence should be addressed.
The views expressed here are those of the authors alone and do not necessarily reflect those of Wellington Management Company LLP. This article is intended to stimulate further research and is not a recommendation for adopting the proposed method.
These authors contributed equally to this work.
Academic Editors: Michał Rubaszek and Gazi Salah Uddin
Received: 10 April 2021 / Revised: 10 May 2021 / Accepted: 12 May 2021 / Published: 16 May 2021
(This article belongs to the Special Issue Forecasting Commodity Markets)
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. View Full-Text
Keywords: Gerber correlation; commodity markets; comovements; CARML models; DCC models; FHS Gerber correlation; commodity markets; comovements; CARML models; DCC models; FHS
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MDPI and ACS Style

Algieri, B.; Leccadito, A.; Toscano, P. A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements. Forecasting 2021, 3, 339-354. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020022

AMA Style

Algieri B, Leccadito A, Toscano P. A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements. Forecasting. 2021; 3(2):339-354. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020022

Chicago/Turabian Style

Algieri, Bernardina, Arturo Leccadito, and Pietro Toscano. 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements" Forecasting 3, no. 2: 339-354. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020022

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