Quantile and Time–Frequency Risk Spillover Between Climate Policy Uncertainty and Grains Commodity Markets Article Swipe
Hongjun Zeng
,
Mohammad Zoynul Abedin
,
Abdullahi D. Ahmed
,
Brian M. Lucey
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/fut.22583
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/fut.22583
This paper aims to study the dynamic risk connection between the Climate Policy Uncertainty Index (CPU) of the United States and the grain commodity market. Our findings denote that (a) quantile spillover is stronger at extreme than median levels, underscoring the value of systematic risk spillovers in extreme market conditions. (b) Wavelet coherence analysis proposes that the structure of the CPU connection with the grain commodity market is heterogeneous at time–frequency scales. (c) Under conditions of market stability, CPU's capability to predict risks in the most segmented grain commodity markets was not as pronounced as in extreme market scenarios. (d) The spillovers between CPU and major grain commodities under diverse quantile states were significantly influenced by climate change. Results from this paper have practical implications for investors managing climate‐related risk exposures and will also assist policymakers in developing countries to develop a sensible policy package.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/fut.22583
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/fut.22583
- OA Status
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- Cited By
- 13
- References
- 82
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https://doi.org/10.1002/fut.22583Digital Object Identifier
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Quantile and Time–Frequency Risk Spillover Between Climate Policy Uncertainty and Grains Commodity MarketsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-02Full publication date if available
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Hongjun Zeng, Mohammad Zoynul Abedin, Abdullahi D. Ahmed, Brian M. LuceyList of authors in order
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https://doi.org/10.1002/fut.22583Publisher landing page
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hybridOpen access status per OpenAlex
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Spillover effect, Quantile, Economics, Commodity, Econometrics, Financial economics, Monetary economics, Macroeconomics, FinanceTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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| abstract_inverted_index.that | 29, 56 |
| abstract_inverted_index.this | 121 |
| abstract_inverted_index.were | 113 |
| abstract_inverted_index.will | 133 |
| abstract_inverted_index.with | 63 |
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| abstract_inverted_index.CPU's | 79 |
| abstract_inverted_index.Index | 15 |
| abstract_inverted_index.Under | 74 |
| abstract_inverted_index.grain | 23, 65, 88, 107 |
| abstract_inverted_index.major | 106 |
| abstract_inverted_index.paper | 2, 122 |
| abstract_inverted_index.risks | 83 |
| abstract_inverted_index.study | 5 |
| abstract_inverted_index.under | 109 |
| abstract_inverted_index.value | 42 |
| abstract_inverted_index.Policy | 13 |
| abstract_inverted_index.States | 20 |
| abstract_inverted_index.United | 19 |
| abstract_inverted_index.assist | 135 |
| abstract_inverted_index.denote | 28 |
| abstract_inverted_index.market | 49, 67, 77, 98 |
| abstract_inverted_index.median | 38 |
| abstract_inverted_index.policy | 144 |
| abstract_inverted_index.states | 112 |
| abstract_inverted_index.Climate | 12 |
| abstract_inverted_index.Results | 119 |
| abstract_inverted_index.Wavelet | 52 |
| abstract_inverted_index.between | 10, 103 |
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| abstract_inverted_index.climate | 117 |
| abstract_inverted_index.develop | 141 |
| abstract_inverted_index.diverse | 110 |
| abstract_inverted_index.dynamic | 7 |
| abstract_inverted_index.extreme | 36, 48, 97 |
| abstract_inverted_index.levels, | 39 |
| abstract_inverted_index.market. | 25 |
| abstract_inverted_index.markets | 90 |
| abstract_inverted_index.predict | 82 |
| abstract_inverted_index.scales. | 72 |
| abstract_inverted_index.ABSTRACT | 0 |
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| abstract_inverted_index.findings | 27 |
| abstract_inverted_index.managing | 128 |
| abstract_inverted_index.package. | 145 |
| abstract_inverted_index.proposes | 55 |
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| abstract_inverted_index.stronger | 34 |
| abstract_inverted_index.coherence | 53 |
| abstract_inverted_index.commodity | 24, 66, 89 |
| abstract_inverted_index.countries | 139 |
| abstract_inverted_index.exposures | 131 |
| abstract_inverted_index.investors | 127 |
| abstract_inverted_index.practical | 124 |
| abstract_inverted_index.segmented | 87 |
| abstract_inverted_index.spillover | 32 |
| abstract_inverted_index.structure | 58 |
| abstract_inverted_index.capability | 80 |
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| abstract_inverted_index.connection | 9, 62 |
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| abstract_inverted_index.influenced | 115 |
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| abstract_inverted_index.scenarios. | 99 |
| abstract_inverted_index.spillovers | 46, 102 |
| abstract_inverted_index.stability, | 78 |
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| abstract_inverted_index.commodities | 108 |
| abstract_inverted_index.conditions. | 50 |
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| abstract_inverted_index.policymakers | 136 |
| abstract_inverted_index.underscoring | 40 |
| abstract_inverted_index.heterogeneous | 69 |
| abstract_inverted_index.significantly | 114 |
| abstract_inverted_index.time–frequency | 71 |
| abstract_inverted_index.climate‐related | 129 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
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| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |