Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model

Sayar Karmakar; Rangan Gupta; Oguzhan Cepni; Lavinia Rognone

doi:10.1016/j.resourpol.2023.103438

Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model

Sayar Karmakar^*, Rangan Gupta, Oguzhan Cepni, Lavinia Rognone

^*Corresponding author for this work

Department of Economics

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

We investigate whether text-based physical or transition climate risks forecast the daily volume of gold trade contracts. Given the count-valued nature of gold volume data, we employ a log-linear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (IN-GARCH) model with a climate-related covariate. We detect that physical risks have a significant predictive power for gold volume at 5- and 22-day-ahead horizons. Additionally, from a full-sample analysis, it emerges that physical risks positively relate with gold volume. Combining these findings, we conclude that gold hedges physical risks at 1-week and 1-month horizons. Similar results hold for platinum and palladium, but not for silver.

Original language	English
Article number	103438
Journal	Resources Policy
Volume	82
Number of pages	8
ISSN	0301-4207
DOIs	https://doi.org/10.1016/j.resourpol.2023.103438
Publication status	Published - May 2023

Keywords

Climate risks
Precious metals
Forecasting
Trading volumes
Count data
INGARCH

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "Climate Risks and Predictability of the Trading Volume of Gold: Evidence from an INGARCH Model",

abstract = "We investigate whether text-based physical or transition climate risks forecast the daily volume of gold trade contracts. Given the count-valued nature of gold volume data, we employ a log-linear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (IN-GARCH) model with a climate-related covariate. We detect that physical risks have a significant predictive power for gold volume at 5- and 22-day-ahead horizons. Additionally, from a full-sample analysis, it emerges that physical risks positively relate with gold volume. Combining these findings, we conclude that gold hedges physical risks at 1-week and 1-month horizons. Similar results hold for platinum and palladium, but not for silver.",

keywords = "Climate risks, Precious metals, Forecasting, Trading volumes, Count data, INGARCH, Climate risks, Precious metals, Forecasting, Trading volumes, Count data, INGARCH",

author = "Sayar Karmakar and Rangan Gupta and Oguzhan Cepni and Lavinia Rognone",

year = "2023",

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doi = "10.1016/j.resourpol.2023.103438",

language = "English",

volume = "82",

journal = "Resources Policy",

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TY - JOUR

T1 - Climate Risks and Predictability of the Trading Volume of Gold

T2 - Evidence from an INGARCH Model

AU - Karmakar, Sayar

AU - Gupta, Rangan

AU - Cepni, Oguzhan

AU - Rognone, Lavinia

PY - 2023/5

Y1 - 2023/5

N2 - We investigate whether text-based physical or transition climate risks forecast the daily volume of gold trade contracts. Given the count-valued nature of gold volume data, we employ a log-linear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (IN-GARCH) model with a climate-related covariate. We detect that physical risks have a significant predictive power for gold volume at 5- and 22-day-ahead horizons. Additionally, from a full-sample analysis, it emerges that physical risks positively relate with gold volume. Combining these findings, we conclude that gold hedges physical risks at 1-week and 1-month horizons. Similar results hold for platinum and palladium, but not for silver.

AB - We investigate whether text-based physical or transition climate risks forecast the daily volume of gold trade contracts. Given the count-valued nature of gold volume data, we employ a log-linear Poisson integer-valued generalized autoregressive conditional heteroskedasticity (IN-GARCH) model with a climate-related covariate. We detect that physical risks have a significant predictive power for gold volume at 5- and 22-day-ahead horizons. Additionally, from a full-sample analysis, it emerges that physical risks positively relate with gold volume. Combining these findings, we conclude that gold hedges physical risks at 1-week and 1-month horizons. Similar results hold for platinum and palladium, but not for silver.

KW - Climate risks

KW - Precious metals

KW - Forecasting

KW - Trading volumes

KW - Count data

KW - INGARCH

KW - Climate risks

KW - Precious metals

KW - Forecasting

KW - Trading volumes

KW - Count data

KW - INGARCH

U2 - 10.1016/j.resourpol.2023.103438

DO - 10.1016/j.resourpol.2023.103438

M3 - Journal article

SN - 0301-4207

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JO - Resources Policy

JF - Resources Policy

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