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

Bernardina Algieri, Arturo Leccadito and Pietro Toscano
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Arturo Leccadito: Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci, 87030 Rende, Italy
Pietro Toscano: Wellington Management Company LLP, 280 Congress Street, Boston, MA 02210, USA

Forecasting, 2021, vol. 3, issue 2, 1-16

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.

Keywords: Gerber correlation; commodity markets; comovements; CARML models; DCC models; FHS (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:3:y:2021:i:2:p:22-354:d:555557

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