Modelling commodity market volatility with climate policy uncertainty: a GARCH-MIDAS approach
This research employs the Generalized Autoregressive Conditional Heteroskedasticity-GARCH option of Mixed Data Sampling – MIDAS (GARCH-MIDAS) model to examine how well commodity return volatility can be predicted using the US climate policy uncertainty (USCPU). Our analysis utilizes 20-day annualize...
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| Published in | SN Business & Economics Vol. 5; no. 3 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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Springer International Publishing
28.02.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2662-9399 2662-9399 |
| DOI | 10.1007/s43546-025-00792-0 |
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| Abstract | This research employs the Generalized Autoregressive Conditional Heteroskedasticity-GARCH option of Mixed Data Sampling – MIDAS (GARCH-MIDAS) model to examine how well commodity return volatility can be predicted using the US climate policy uncertainty (USCPU). Our analysis utilizes 20-day annualized realized volatility returns for nine global commodities (including Aluminium, Cocoa, Coffee, Copper, Cotton, Rice, Soybean, Sugar, and Wheat) to develop the predictability model, with USCPU as the predictor. The outcomes of our investigation consistently show a considerable direct nexus between USCPU and the selected commodities. In other words, this implies that USCPU is a strong predictor of volatility in commodity returns. Therefore, our results offer implications for the pivotal role of climate change policies in influencing trading activities in the commodity market. Additionally, for robustness, we subject our data to further analysis using the economic policy uncertainty (EPU) index. This is to ascertain whether our results are index-sensitive or not, expectedly, our result shows consistency with the earlier observed pattern for CPU and confirms that our result are not sensitive to the choice of the indicator. These outcomes underscore the crucial impact of climate change considerations in investment decisions and the significant effect of economic policy uncertainty on economic and investment choices. |
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| AbstractList | This research employs the Generalized Autoregressive Conditional Heteroskedasticity-GARCH option of Mixed Data Sampling – MIDAS (GARCH-MIDAS) model to examine how well commodity return volatility can be predicted using the US climate policy uncertainty (USCPU). Our analysis utilizes 20-day annualized realized volatility returns for nine global commodities (including Aluminium, Cocoa, Coffee, Copper, Cotton, Rice, Soybean, Sugar, and Wheat) to develop the predictability model, with USCPU as the predictor. The outcomes of our investigation consistently show a considerable direct nexus between USCPU and the selected commodities. In other words, this implies that USCPU is a strong predictor of volatility in commodity returns. Therefore, our results offer implications for the pivotal role of climate change policies in influencing trading activities in the commodity market. Additionally, for robustness, we subject our data to further analysis using the economic policy uncertainty (EPU) index. This is to ascertain whether our results are index-sensitive or not, expectedly, our result shows consistency with the earlier observed pattern for CPU and confirms that our result are not sensitive to the choice of the indicator. These outcomes underscore the crucial impact of climate change considerations in investment decisions and the significant effect of economic policy uncertainty on economic and investment choices. |
| ArticleNumber | 29 |
| Author | Olaniran, Abeeb O. Ngwu, Franklin N. Nnamdi, Kelechi C. Taliat, Mohammed K. Lasisi, Lukman A. |
| Author_xml | – sequence: 1 givenname: Lukman A. orcidid: 0000-0002-8354-7903 surname: Lasisi fullname: Lasisi, Lukman A. email: abisoyelasisi2002@gmail.com, llasisi@lbs.edu.ng organization: Lagos Business School Public Sector Initiative – sequence: 2 givenname: Franklin N. surname: Ngwu fullname: Ngwu, Franklin N. organization: Lagos Business School Public Sector Initiative – sequence: 3 givenname: Mohammed K. surname: Taliat fullname: Taliat, Mohammed K. organization: University of Abuja – sequence: 4 givenname: Abeeb O. surname: Olaniran fullname: Olaniran, Abeeb O. organization: Centre for Econometrics and Applied Research – sequence: 5 givenname: Kelechi C. surname: Nnamdi fullname: Nnamdi, Kelechi C. organization: Lagos Business School Public Sector Initiative |
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| Title | Modelling commodity market volatility with climate policy uncertainty: a GARCH-MIDAS approach |
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