Pub Date : 2026-03-01Epub Date: 2026-01-23DOI: 10.1016/j.jcomm.2026.100542
John Hua Fan , Adrian Fernandez-Perez , Ivan Indriawan , Neda Todorova
We adopt a data-driven approach to examine uranium price explosiveness. We detect explosive episodes across varying durations and apply a LASSO-Logit framework to uncover key variables associated with price explosiveness. Our findings reveal that uranium price explosiveness is persistent, with positive explosiveness dominating and lasting an average of ten months. Variables such as dividend growth, monetary conditions, and expansion in the uranium sector significantly increase the likelihood of explosiveness. Additionally, uncertainty and geopolitical risks shape market dynamics. A local projections approach highlights that monetary tightening and uranium price momentum can sustain upward price pressures, while economic activity and sovereign debt risks exert downward forces. As uranium becomes increasingly vital to the transition toward a net-zero economy, our findings help bring greater transparency to a traditionally opaque commodity market.
{"title":"Boom, bust, and Fission: A Deep Dive into Uranium price explosiveness","authors":"John Hua Fan , Adrian Fernandez-Perez , Ivan Indriawan , Neda Todorova","doi":"10.1016/j.jcomm.2026.100542","DOIUrl":"10.1016/j.jcomm.2026.100542","url":null,"abstract":"<div><div>We adopt a data-driven approach to examine uranium price explosiveness. We detect explosive episodes across varying durations and apply a LASSO-Logit framework to uncover key variables associated with price explosiveness. Our findings reveal that uranium price explosiveness is persistent, with positive explosiveness dominating and lasting an average of ten months. Variables such as dividend growth, monetary conditions, and expansion in the uranium sector significantly increase the likelihood of explosiveness. Additionally, uncertainty and geopolitical risks shape market dynamics. A local projections approach highlights that monetary tightening and uranium price momentum can sustain upward price pressures, while economic activity and sovereign debt risks exert downward forces. As uranium becomes increasingly vital to the transition toward a net-zero economy, our findings help bring greater transparency to a traditionally opaque commodity market.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100542"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-25DOI: 10.1016/j.jcomm.2025.100534
Zhenhua Liu , Hongyu Zhong , Deyuan Zhang
This study investigates the dynamic impacts of climate policy uncertainty and investor behavior on carbon market returns by using the quasi-Bayes local likelihood time-varying parameter vector autoregression (QBLL-TVP-VAR) model. The empirical results indicate that climate policy uncertainty has significant time-varying impacts on carbon market returns, which is more critical during major international climate events. Moreover, investor behavior provides a transmission channel for the propagation between climate policy uncertainty shocks and the carbon market, but the role of different types of investor behavior is heterogeneous. These findings highlight the need to consider the nonlinear impact of climate policy uncertainty on the carbon market.
{"title":"Climate policy uncertainty, investor behavior, and carbon market returns","authors":"Zhenhua Liu , Hongyu Zhong , Deyuan Zhang","doi":"10.1016/j.jcomm.2025.100534","DOIUrl":"10.1016/j.jcomm.2025.100534","url":null,"abstract":"<div><div>This study investigates the dynamic impacts of climate policy uncertainty and investor behavior on carbon market returns by using the quasi-Bayes local likelihood time-varying parameter vector autoregression (QBLL-TVP-VAR) model. The empirical results indicate that climate policy uncertainty has significant time-varying impacts on carbon market returns, which is more critical during major international climate events. Moreover, investor behavior provides a transmission channel for the propagation between climate policy uncertainty shocks and the carbon market, but the role of different types of investor behavior is heterogeneous. These findings highlight the need to consider the nonlinear impact of climate policy uncertainty on the carbon market.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100534"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-04DOI: 10.1016/j.jcomm.2025.100537
Anabelle Couleau , Andres Trujillo-Barrera , Xiaoli Etienne
The Coffee ‘C’ futures contract traded on the InterContinental Exchange is recognized as the global benchmark for coffee pricing. Using high-frequency tick data from January 2010 to November 2021, we document the presence and drivers of intraday market momentum for the Coffee ‘C’ futures contract. Intraday market momentum is time-varying and emerges primarily during periods of elevated volatility and concentrated in the upper tails of the return distribution. Persistence tests indicate that early-day momentum linked to overnight and morning returns tends to carry over across days, consistent with gradual information incorporation and the late-informed trading hypothesis, while late-day momentum dissipates quickly, reflecting portfolio rebalancing and hedging pressures. Regression evidence suggests that stronger intraday momentum tend to coincide with faster quoting activity, greater opening liquidity demand, and lower speculative participation.
{"title":"Intraday market momentum in coffee futures: Dynamics and drivers","authors":"Anabelle Couleau , Andres Trujillo-Barrera , Xiaoli Etienne","doi":"10.1016/j.jcomm.2025.100537","DOIUrl":"10.1016/j.jcomm.2025.100537","url":null,"abstract":"<div><div>The Coffee ‘C’ futures contract traded on the InterContinental Exchange is recognized as the global benchmark for coffee pricing. Using high-frequency tick data from January 2010 to November 2021, we document the presence and drivers of intraday market momentum for the Coffee ‘C’ futures contract. Intraday market momentum is time-varying and emerges primarily during periods of elevated volatility and concentrated in the upper tails of the return distribution. Persistence tests indicate that early-day momentum linked to overnight and morning returns tends to carry over across days, consistent with gradual information incorporation and the late-informed trading hypothesis, while late-day momentum dissipates quickly, reflecting portfolio rebalancing and hedging pressures. Regression evidence suggests that stronger intraday momentum tend to coincide with faster quoting activity, greater opening liquidity demand, and lower speculative participation.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100537"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145692654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-11-26DOI: 10.1016/j.jcomm.2025.100536
Kejia Yan , Boqiang Lin
This study investigates the dynamic spillover effects between European Union Allowance (EUA) futures and major commodity futures, including energy, agricultural products, and precious metals, across all four phases of the EU ETS. Using the quantile connectedness framework, we capture heterogeneous and asymmetric transmission mechanisms under different market regimes. The results show that EUA futures are predominantly net receivers of shocks from global commodity markets, reflecting their strong integration with energy and financial systems. Nevertheless, EUAs act as net transmitters to fossil fuels—particularly natural gas and coal—under specific conditions, while spillovers to wheat occur mainly in extreme positive markets, raising concerns over food affordability. These findings highlight that EUA prices within the 20 %–60 % quantile range are “reasonable,” effectively constraining fossil fuels without destabilising agricultural or precious metal markets. Overall, the study enriches carbon finance literature by extending connectedness methods to the EU ETS, demonstrating that carbon markets function not only as emission reduction instruments but also as key nodes in global commodity interdependence.
{"title":"Carbon pricing, commodity markets, and economic stability: Evidence from the EU ETS","authors":"Kejia Yan , Boqiang Lin","doi":"10.1016/j.jcomm.2025.100536","DOIUrl":"10.1016/j.jcomm.2025.100536","url":null,"abstract":"<div><div>This study investigates the dynamic spillover effects between European Union Allowance (EUA) futures and major commodity futures, including energy, agricultural products, and precious metals, across all four phases of the EU ETS. Using the quantile connectedness framework, we capture heterogeneous and asymmetric transmission mechanisms under different market regimes. The results show that EUA futures are predominantly net receivers of shocks from global commodity markets, reflecting their strong integration with energy and financial systems. Nevertheless, EUAs act as net transmitters to fossil fuels—particularly natural gas and coal—under specific conditions, while spillovers to wheat occur mainly in extreme positive markets, raising concerns over food affordability. These findings highlight that EUA prices within the 20 %–60 % quantile range are “reasonable,” effectively constraining fossil fuels without destabilising agricultural or precious metal markets. Overall, the study enriches carbon finance literature by extending connectedness methods to the EU ETS, demonstrating that carbon markets function not only as emission reduction instruments but also as key nodes in global commodity interdependence.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100536"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-23DOI: 10.1016/j.jcomm.2026.100541
Niaz Bashiri Behmiri , Carlo Fezzi , Francesco Ravazzolo
This study examines short-to mid-term point forecasting of daily electricity prices, with particular emphasis on the role of renewable energy sources. We use data from the market zone corresponding to the Northern region of Italy, applying both time series and machine learning methodologies. The forecasts are evaluated for two individual years, 2019 and 2024. In 2019, traditional energy variables such as electricity load, natural gas prices, and imports, were the primary drivers of forecast accuracy. During this period, adding renewable energy production data offered negligible benefits, with solar and wind contributing only marginally. By contrast, in 2024, market volatility increased greatly due to geopolitical conflicts and increased renewable energy integration. Under these conditions, while solar and wind still added limited value, hydropower improved forecast accuracy substantially. The results suggest that the role of renewable energy sources in electricity price forecasting is growing. However, their predictive power is influenced by their market share and by their variability and predictability.
{"title":"Renewable sources and short-to-mid-term electricity price forecasting","authors":"Niaz Bashiri Behmiri , Carlo Fezzi , Francesco Ravazzolo","doi":"10.1016/j.jcomm.2026.100541","DOIUrl":"10.1016/j.jcomm.2026.100541","url":null,"abstract":"<div><div>This study examines short-to mid-term point forecasting of daily electricity prices, with particular emphasis on the role of renewable energy sources. We use data from the market zone corresponding to the Northern region of Italy, applying both time series and machine learning methodologies. The forecasts are evaluated for two individual years, 2019 and 2024. In 2019, traditional energy variables such as electricity load, natural gas prices, and imports, were the primary drivers of forecast accuracy. During this period, adding renewable energy production data offered negligible benefits, with solar and wind contributing only marginally. By contrast, in 2024, market volatility increased greatly due to geopolitical conflicts and increased renewable energy integration. Under these conditions, while solar and wind still added limited value, hydropower improved forecast accuracy substantially. The results suggest that the role of renewable energy sources in electricity price forecasting is growing. However, their predictive power is influenced by their market share and by their variability and predictability.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100541"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-31DOI: 10.1016/j.jcomm.2025.100535
Pedro Gavronski , Alan De Genaro
This paper develops a continuous-time framework for pricing electricity future contracts that addresses key limitations of traditional models, particularly their inability to capture price spikes and shifts in hedging behavior. The proposed model incorporates both jump components and a time-varying drift to reflect dynamic changes in supply and demand for hedging. Additionally, correlated Brownian motions are included to capture common shocks across contracts with different delivery periods. Model parameters are estimated using the generalized method of moments (GMM) on daily settlement data from the Norwegian power market. Monte Carlo simulations confirm the consistency and robustness of the estimators. Out-of-sample forecasting exercises demonstrate superior predictive performance relative to standard ARMA-GARCH benchmarks. The results underscore the model’s practical relevance for traders and risk managers engaged in electricity portfolio management.
{"title":"Jumps and jolts: A continuous-time model for electricity future contract pricing","authors":"Pedro Gavronski , Alan De Genaro","doi":"10.1016/j.jcomm.2025.100535","DOIUrl":"10.1016/j.jcomm.2025.100535","url":null,"abstract":"<div><div>This paper develops a continuous-time framework for pricing electricity future contracts that addresses key limitations of traditional models, particularly their inability to capture price spikes and shifts in hedging behavior. The proposed model incorporates both jump components and a time-varying drift to reflect dynamic changes in supply and demand for hedging. Additionally, correlated Brownian motions are included to capture common shocks across contracts with different delivery periods. Model parameters are estimated using the generalized method of moments (GMM) on daily settlement data from the Norwegian power market. Monte Carlo simulations confirm the consistency and robustness of the estimators. Out-of-sample forecasting exercises demonstrate superior predictive performance relative to standard ARMA-GARCH benchmarks. The results underscore the model’s practical relevance for traders and risk managers engaged in electricity portfolio management.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100535"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-10DOI: 10.1016/j.jcomm.2026.100539
Carlos P. Maquieira , Boris Pastén-Henríquez
This research analyzes the association between gold-mining stock returns and climate policy uncertainty (CPU) and examines whether CPU moderates the relationship between gold returns and gold-mining stock returns. Using monthly data for 68 gold-mining companies from nine countries over the period 2011–2022, we report that CPU exerts a significant and adverse effect on gold-mining stock returns, diminishing the positive impact of gold returns on gold stock performance. In contrast, Global Economic Policy Uncertainty (GEPU), Monetary Policy Uncertainty (MPU), and Fiscal Policy Uncertainty (FPU) are positively associated with gold-mining stock returns and strengthen the relationship between gold returns and mining stock performance, whereas Local Economic Policy Uncertainty (LEPU) does not exhibit a significant association. The results remain robust after correcting for endogeneity using an instrumental variable approach. Extending the analysis to energy transition metals, including copper, lithium, nickel, and cobalt, we find that climate policy uncertainty is positively associated with stock returns in these sectors and depending on time windows we find a negative impact of CPU on the link between metal returns and metal-stock returns.
{"title":"Does climate policy uncertainty impact gold-mining stock returns? International evidence","authors":"Carlos P. Maquieira , Boris Pastén-Henríquez","doi":"10.1016/j.jcomm.2026.100539","DOIUrl":"10.1016/j.jcomm.2026.100539","url":null,"abstract":"<div><div>This research analyzes the association between gold-mining stock returns and climate policy uncertainty (CPU) and examines whether CPU moderates the relationship between gold returns and gold-mining stock returns. Using monthly data for 68 gold-mining companies from nine countries over the period 2011–2022, we report that CPU exerts a significant and adverse effect on gold-mining stock returns, diminishing the positive impact of gold returns on gold stock performance. In contrast, Global Economic Policy Uncertainty (GEPU), Monetary Policy Uncertainty (MPU), and Fiscal Policy Uncertainty (FPU) are positively associated with gold-mining stock returns and strengthen the relationship between gold returns and mining stock performance, whereas Local Economic Policy Uncertainty (LEPU) does not exhibit a significant association. The results remain robust after correcting for endogeneity using an instrumental variable approach. Extending the analysis to energy transition metals, including copper, lithium, nickel, and cobalt, we find that climate policy uncertainty is positively associated with stock returns in these sectors and depending on time windows we find a negative impact of CPU on the link between metal returns and metal-stock returns.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100539"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-16DOI: 10.1016/j.jcomm.2026.100540
Andrianos E. Tsekrekos, Konstantinos I. Vasileiadis
Oil price changes have been considered a good (negative) predictor of stock market returns. In this study, we show via predictive regressions, for an extensive dataset of 44 developed and developing stock markets, that this negative relationship is present only up to the global financial crisis and has largely disappeared ever since. We document an evident shift in the predictive behavior of oil price changes after the 2008 global financial crisis, especially in developed stock markets, a finding that is robust to several additional tests we perform. A possible explanation of the change in the oil-stock return relationship post 2008 could be the increased significance of industrial metals (mainly copper and aluminum). We show that after 2008, industrial metals price changes have gained significance as stock market predictors, mainly in recessions, a finding that is partly consistent with the existing literature on stock market predictability.
{"title":"Oil prices as a predictor of stock market returns","authors":"Andrianos E. Tsekrekos, Konstantinos I. Vasileiadis","doi":"10.1016/j.jcomm.2026.100540","DOIUrl":"10.1016/j.jcomm.2026.100540","url":null,"abstract":"<div><div>Oil price changes have been considered a good (negative) predictor of stock market returns. In this study, we show via predictive regressions, for an extensive dataset of 44 developed and developing stock markets, that this negative relationship is present only up to the global financial crisis and has largely disappeared ever since. We document an evident shift in the predictive behavior of oil price changes after the 2008 global financial crisis, especially in developed stock markets, a finding that is robust to several additional tests we perform. A possible explanation of the change in the oil-stock return relationship post 2008 could be the increased significance of industrial metals (mainly copper and aluminum). We show that after 2008, industrial metals price changes have gained significance as stock market predictors, mainly in recessions, a finding that is partly consistent with the existing literature on stock market predictability.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100540"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.jcomm.2026.100545
Ilia Bouchouev , Brett Johnson , Wu-Yen Sun
The paper applies the recently developed quadratic normal model (QNM) to oil options. This three-parameter model assumes parabolic local volatility and extends the Bachelier model to a much broader class of “fat-tailed” distributions with two additional parameters related to skewness and kurtosis. The primary focus of this paper is to demonstrate how this model can be efficiently calibrated to option prices and used by volatility arbitrageurs in managing their portfolios. We calibrate model parameters daily to market prices of WTI options over an extensive twenty-five year period and analyze the dynamics and stability of parameters over time. We show two primary applications of the model for delta-hedging of market-making portfolios and for pricing over-the-counter options.
{"title":"Volatility trading with the quadratic normal model in the oil options market","authors":"Ilia Bouchouev , Brett Johnson , Wu-Yen Sun","doi":"10.1016/j.jcomm.2026.100545","DOIUrl":"10.1016/j.jcomm.2026.100545","url":null,"abstract":"<div><div>The paper applies the recently developed quadratic normal model (QNM) to oil options. This three-parameter model assumes parabolic local volatility and extends the Bachelier model to a much broader class of “fat-tailed” distributions with two additional parameters related to skewness and kurtosis. The primary focus of this paper is to demonstrate how this model can be efficiently calibrated to option prices and used by volatility arbitrageurs in managing their portfolios. We calibrate model parameters daily to market prices of WTI options over an extensive twenty-five year period and analyze the dynamics and stability of parameters over time. We show two primary applications of the model for delta-hedging of market-making portfolios and for pricing over-the-counter options.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100545"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-30DOI: 10.1016/j.jcomm.2026.100544
Muhammad Zubair Chishti , Mariya Gubareva , Oktay Özkan , Sorphasith Xaisongkham , Xuan Vinh Vo
We explore the dynamic effects of the global common volatility (GCV) on selected precious metals and energy commodities by employing the two advanced econometric methods: Fourier quantile-on-quantile regression and Fourier quantile regression. GCV induces volatility across gold, silver, and platinum markets in the short-, medium-, and long-term. However, short-term volatility in the silver market exhibits a negative relationship with GCV under both bearish and bullish conditions. Additionally, the oil, gas, and heating oil markets experience substantial losses due to GCV, with the impact intensifying from the short-to long-term across various market states. Moreover, the COVID-19 crisis and the ongoing Russia–Ukraine conflict have markedly strengthened volatility in precious metal and energy markets, reflecting an elevated level of GCV. Nonetheless, the natural gas markets exhibit a negative long-run relationship with GCV during the Russia-Ukraine conflict. Overall, our results underscore a strong interconnectedness between GCV and precious metals and energy markets, highlighting significant risks that global financial market volatility poses to these sectors.
{"title":"Dynamic effects of the global common volatility on precious metals and energy markets: Fourier quantile-on-quantile and Fourier quantile regressions","authors":"Muhammad Zubair Chishti , Mariya Gubareva , Oktay Özkan , Sorphasith Xaisongkham , Xuan Vinh Vo","doi":"10.1016/j.jcomm.2026.100544","DOIUrl":"10.1016/j.jcomm.2026.100544","url":null,"abstract":"<div><div>We explore the dynamic effects of the global common volatility (GCV) on selected precious metals and energy commodities by employing the two advanced econometric methods: Fourier quantile-on-quantile regression and Fourier quantile regression. GCV induces volatility across gold, silver, and platinum markets in the short-, medium-, and long-term. However, short-term volatility in the silver market exhibits a negative relationship with GCV under both bearish and bullish conditions. Additionally, the oil, gas, and heating oil markets experience substantial losses due to GCV, with the impact intensifying from the short-to long-term across various market states. Moreover, the COVID-19 crisis and the ongoing Russia–Ukraine conflict have markedly strengthened volatility in precious metal and energy markets, reflecting an elevated level of GCV. Nonetheless, the natural gas markets exhibit a negative long-run relationship with GCV during the Russia-Ukraine conflict. Overall, our results underscore a strong interconnectedness between GCV and precious metals and energy markets, highlighting significant risks that global financial market volatility poses to these sectors.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"41 ","pages":"Article 100544"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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