Pub Date : 2025-01-11DOI: 10.1016/j.jcomm.2025.100458
Augusto Seabra Santos, Alexandre Nunes Almeida
This study explores the relationship between speculators and the volatility of live cattle futures in Brazil, focusing on two distinct categories of speculation: day traders (and scalpers) and institutional investors. Analyzing the nearest and October contracts from 2006 to 2019, the research employs the ARIMA-GARCH methodology to estimate volatilities. Additional analyses are conducted to estimate the expected and unexpected effects of speculators on the previously determined volatility levels. Our findings indicate that day trader speculators heighten the volatility of contracts nearing expiration, primarily due to their unexpected actions and limited market information usage. They tend to buy high and sell low. In contrast, institutional investors, with access to more comprehensive information, have a moderate influence on volatility, capable of strategically maneuvering market distortions. The accuracy of the conclusions is strengthened by robustness and placebo tests.
{"title":"Do different speculation strategies cause distinct impacts on the volatility of the live cattle futures in Brazil?","authors":"Augusto Seabra Santos, Alexandre Nunes Almeida","doi":"10.1016/j.jcomm.2025.100458","DOIUrl":"10.1016/j.jcomm.2025.100458","url":null,"abstract":"<div><div>This study explores the relationship between speculators and the volatility of live cattle futures in Brazil, focusing on two distinct categories of speculation: day traders (and scalpers) and institutional investors. Analyzing the nearest and October contracts from 2006 to 2019, the research employs the ARIMA-GARCH methodology to estimate volatilities. Additional analyses are conducted to estimate the expected and unexpected effects of speculators on the previously determined volatility levels. Our findings indicate that day trader speculators heighten the volatility of contracts nearing expiration, primarily due to their unexpected actions and limited market information usage. They tend to buy high and sell low. In contrast, institutional investors, with access to more comprehensive information, have a moderate influence on volatility, capable of strategically maneuvering market distortions. The accuracy of the conclusions is strengthened by robustness and placebo tests.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"37 ","pages":"Article 100458"},"PeriodicalIF":3.7,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181346","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 : 2024-11-29DOI: 10.1016/j.jcomm.2024.100449
Katarzyna Chȩć, Bartosz Uniejewski, Rafał Weron
Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 h. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3% to 15% in terms of the root mean squared error and exceed 1% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage.
{"title":"Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market","authors":"Katarzyna Chȩć, Bartosz Uniejewski, Rafał Weron","doi":"10.1016/j.jcomm.2024.100449","DOIUrl":"10.1016/j.jcomm.2024.100449","url":null,"abstract":"<div><div>Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce a novel approach which extracts the trend-seasonal pattern from a price series extrapolated using price forecasts for the next 24 h. We assess it using two 5-year long test periods from the German and Spanish power markets, covering the Covid-19 pandemic, the 2021/2022 energy crisis, and the war in Ukraine. Considering parsimonious autoregressive and LASSO-estimated models, we find that improvements in predictive accuracy range from 3% to 15% in terms of the root mean squared error and exceed 1% in terms of profits from a realistic trading strategy involving day-ahead bidding and battery storage.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"37 ","pages":"Article 100449"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181344","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 : 2024-11-16DOI: 10.1016/j.jcomm.2024.100448
Håkan Jankensgård , Nicoletta Marinelli , Rafael Schiozer
Hedging maturity, i.e., how far out in time hedging activities stretch, is an important yet under-investigated aspect of corporate risk management. In this article, we analyse firms’ hedging maturity decision and carry out a comprehensive empirical analysis. We develop three hypotheses to explain hedging maturity. The collateral hypothesis states that longer maturities are predicated on the availability of internal resources that serve as collateral in a hedging transaction. The matching hypothesis argues that firms match their hedging maturity with the maturity of their debt and investment portfolios. The flexibility hypothesis holds that the ability to change operations or investment strategies at low cost is conducive to shorter maturities. Using hand-collected data on derivative positions in the oil and gas industry, we find evidence consistent with all three hypotheses.
{"title":"Time to get mature: Collateral, flexibility and the hedging horizon decision","authors":"Håkan Jankensgård , Nicoletta Marinelli , Rafael Schiozer","doi":"10.1016/j.jcomm.2024.100448","DOIUrl":"10.1016/j.jcomm.2024.100448","url":null,"abstract":"<div><div>Hedging maturity, <em>i.e.</em>, how far out in time hedging activities stretch, is an important yet under-investigated aspect of corporate risk management. In this article, we analyse firms’ hedging maturity decision and carry out a comprehensive empirical analysis. We develop three hypotheses to explain hedging maturity. The collateral hypothesis states that longer maturities are predicated on the availability of internal resources that serve as collateral in a hedging transaction. The matching hypothesis argues that firms match their hedging maturity with the maturity of their debt and investment portfolios. The flexibility hypothesis holds that the ability to change operations or investment strategies at low cost is conducive to shorter maturities. Using hand-collected data on derivative positions in the oil and gas industry, we find evidence consistent with all three hypotheses.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"37 ","pages":"Article 100448"},"PeriodicalIF":3.7,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181345","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 : 2024-11-14DOI: 10.1016/j.jcomm.2024.100447
Qiao Wang
This paper examines whether the carbon pricing risk factor is priced in the cross-section of commodity futures. By analyzing unexpected pricing shocks in carbon emission allowances, carbon pricing risk is indeed priced in commodity futures, with a significant positive risk premium. The analysis of carbon pricing risk loadings reveals that individual commodities' sensitivities to carbon pricing risk vary. Additionally, commodity-specific characteristics, such as basis and hedging pressure, impact these risk loadings. Finally, I demonstrate that a portfolio of commodity futures constructed based on carbon pricing beta provides superior out-of-sample hedging performance for climate change risk compared to alternative hedge portfolios using equities or ETFs.
{"title":"Carbon pricing and the commodity risk premium","authors":"Qiao Wang","doi":"10.1016/j.jcomm.2024.100447","DOIUrl":"10.1016/j.jcomm.2024.100447","url":null,"abstract":"<div><div>This paper examines whether the carbon pricing risk factor is priced in the cross-section of commodity futures. By analyzing unexpected pricing shocks in carbon emission allowances, carbon pricing risk is indeed priced in commodity futures, with a significant positive risk premium. The analysis of carbon pricing risk loadings reveals that individual commodities' sensitivities to carbon pricing risk vary. Additionally, commodity-specific characteristics, such as basis and hedging pressure, impact these risk loadings. Finally, I demonstrate that a portfolio of commodity futures constructed based on carbon pricing beta provides superior out-of-sample hedging performance for climate change risk compared to alternative hedge portfolios using equities or ETFs.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100447"},"PeriodicalIF":3.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651526","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 : 2024-11-13DOI: 10.1016/j.jcomm.2024.100446
Alexander Kurov , Eric Olson , Marketa Halova Wolfe
We reexamine the contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy from 2005 to 2022. Our study offers two main contributions. First, we generalize a novel identification approach based on exogenous intraday shifts in the volatility in futures markets from two markets to multiple markets. Second, we examine contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy. We show that the coefficients measuring contemporaneous causality have substantially changed over time. Specifically, we find that since 2008 stock returns affect crude oil returns. This time variation is also evident in the effect of monetary policy on the crude oil returns. We show that this time variation is consistent with two explanations: the zero lower bound (ZLB) and increased synchronization of crude oil prices with the business cycle.
{"title":"Have the causal effects between equities, oil prices, and monetary policy changed over time?","authors":"Alexander Kurov , Eric Olson , Marketa Halova Wolfe","doi":"10.1016/j.jcomm.2024.100446","DOIUrl":"10.1016/j.jcomm.2024.100446","url":null,"abstract":"<div><div>We reexamine the contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy from 2005 to 2022. Our study offers two main contributions. First, we generalize a novel identification approach based on exogenous intraday shifts in the volatility in futures markets from two markets to multiple markets. Second, we examine contemporaneous causal effects between the U.S. stock prices, crude oil prices, and monetary policy. We show that the coefficients measuring contemporaneous causality have substantially changed over time. Specifically, we find that since 2008 stock returns affect crude oil returns. This time variation is also evident in the effect of monetary policy on the crude oil returns. We show that this time variation is consistent with two explanations: the zero lower bound (ZLB) and increased synchronization of crude oil prices with the business cycle.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100446"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651525","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 : 2024-11-08DOI: 10.1016/j.jcomm.2024.100442
Ingrid Emilie Flessum Ringstad , Kyriaki Tselika
In this paper, we investigate the time and frequency dynamics of connectedness among green assets such as green bonds, clean energy markets, and carbon prices. Using daily price data, we explore return spillovers across these green financial markets by applying the novel framework on time and frequency dynamics proposed by Baruník and Krehlík (2018). This allows us to identify the direction of spillovers among our variables, and decompose the connectedness to differentiate between short-term and long-term return spillovers. Our results indicate that green bonds and carbon prices act as net receivers of shocks, but mainly in the short-term. We also observe a low level of connectedness among our clean energy markets across both low and high frequency bands, even during times of economic or political crisis. Additionally, there are periods in which connectedness between the clean energy assets is driven by the long-term. In periods of economic and political stability, carbon prices may also provide an interesting diversifying tool for short-term investors. Our results should be of interest for investors and portfolio managers who focus on green financial markets, by strengthening the notion that green financial markets can offer diversification opportunities, for both short-term and long-term investors. Policy makers could also benefit from our insights on conectedness in their work on short-term and long-term climate policies. This paper is the first to use this framework to investigate systematic risks within green financial markets.
{"title":"Connectedness between green bonds, clean energy markets and carbon quota prices: Time and frequency dynamics","authors":"Ingrid Emilie Flessum Ringstad , Kyriaki Tselika","doi":"10.1016/j.jcomm.2024.100442","DOIUrl":"10.1016/j.jcomm.2024.100442","url":null,"abstract":"<div><div>In this paper, we investigate the time and frequency dynamics of connectedness among green assets such as green bonds, clean energy markets, and carbon prices. Using daily price data, we explore return spillovers across these green financial markets by applying the novel framework on time and frequency dynamics proposed by Baruník and Krehlík (2018). This allows us to identify the direction of spillovers among our variables, and decompose the connectedness to differentiate between short-term and long-term return spillovers. Our results indicate that green bonds and carbon prices act as net receivers of shocks, but mainly in the short-term. We also observe a low level of connectedness among our clean energy markets across both low and high frequency bands, even during times of economic or political crisis. Additionally, there are periods in which connectedness between the clean energy assets is driven by the long-term. In periods of economic and political stability, carbon prices may also provide an interesting diversifying tool for short-term investors. Our results should be of interest for investors and portfolio managers who focus on green financial markets, by strengthening the notion that green financial markets can offer diversification opportunities, for both short-term and long-term investors. Policy makers could also benefit from our insights on conectedness in their work on short-term and long-term climate policies. This paper is the first to use this framework to investigate systematic risks within green financial markets.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100442"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate systemic risk and spillovers in the commodity network during left-tail events using state-of-the-art methodologies: the Component Exponent Shortfall (CES), Quantile-Vector Autoregression (QVAR) and Causality-in-Risk. Our analysis focuses on five commodity groups: Energy (Crude Oil, Heating Oil, Natural Gas, Coal), Base Metals (Aluminum, Copper, Nickel, Zinc), Ferrous Metals (Iron, Steel), Precious Metals (Gold, Palladium, Platinum, Silver), and Others (Rubber). Across the models utilized, we consistently find that energy commodities and precious metals, along with copper as a standalone commodity, represent the most systemically risky group. Thus, portfolios incorporating these commodities are advised to implement more careful diversification to mitigate risks stemming from systemic factors. This may require additional attention to precious metals, as they are often considered safe-haven assets. Expediting the implementation of regulations that promote the replacement of fossil energy sources with green alternatives could be instrumental in managing systemic risk in the commodity market while also facilitating global sustainability. Finally, the results show that the impact of the Israeli-Palestinian conflict on both systemic risk and spillovers has been limited compared to the effects of COVID-19 and the Russia-Ukraine war.
{"title":"Commodity market downturn: Systemic risk and spillovers during left tail events","authors":"Samet Gunay , Destan Kirimhan , Emrah Ismail Cevik","doi":"10.1016/j.jcomm.2024.100445","DOIUrl":"10.1016/j.jcomm.2024.100445","url":null,"abstract":"<div><div>We investigate systemic risk and spillovers in the commodity network during left-tail events using state-of-the-art methodologies: the Component Exponent Shortfall (CES), Quantile-Vector Autoregression (QVAR) and Causality-in-Risk. Our analysis focuses on five commodity groups: Energy (Crude Oil, Heating Oil, Natural Gas, Coal), Base Metals (Aluminum, Copper, Nickel, Zinc), Ferrous Metals (Iron, Steel), Precious Metals (Gold, Palladium, Platinum, Silver), and Others (Rubber). Across the models utilized, we consistently find that energy commodities and precious metals, along with copper as a standalone commodity, represent the most systemically risky group. Thus, portfolios incorporating these commodities are advised to implement more careful diversification to mitigate risks stemming from systemic factors. This may require additional attention to precious metals, as they are often considered safe-haven assets. Expediting the implementation of regulations that promote the replacement of fossil energy sources with green alternatives could be instrumental in managing systemic risk in the commodity market while also facilitating global sustainability. Finally, the results show that the impact of the Israeli-Palestinian conflict on both systemic risk and spillovers has been limited compared to the effects of COVID-19 and the Russia-Ukraine war.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100445"},"PeriodicalIF":3.7,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651524","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 : 2024-10-29DOI: 10.1016/j.jcomm.2024.100444
Kaixin Li , Zhikai Zhang , Yudong Wang , Yaojie Zhang
We construct North American oil-related industry ESG indices based on Elastic Net and PCA/SPCA/PLS dimensionality reduction techniques. We discover that the ESG indices show significant forecasting power for crude oil returns both in- and out-of-sample, and their ability to significantly predict oil returns remains when the delayed ESG release is considered. Additionally, our analysis suggests that the predictive abilities of ESG indices remain robust and unaffected by stock returns in the oil-related industry. The ESG indices can provide information that is heterogeneous and complementary to macroeconomic variables and technical indicators. Based on the analysis over the business cycle, ESG indices show predictability in forecasting crude oil returns during economic expansions rather than recessions. Moreover, ESG indices' predictive ability is also of economic significance, as shown by the substantial economic value it generates for mean-variance investors. Finally, we explore the potential economic channels, and the result reveals that the predictive power of ESG indices arises from speculative behavior in the oil market and oil demand.
{"title":"Forecasting crude oil returns with oil-related industry ESG indices","authors":"Kaixin Li , Zhikai Zhang , Yudong Wang , Yaojie Zhang","doi":"10.1016/j.jcomm.2024.100444","DOIUrl":"10.1016/j.jcomm.2024.100444","url":null,"abstract":"<div><div>We construct North American oil-related industry ESG indices based on Elastic Net and PCA/SPCA/PLS dimensionality reduction techniques. We discover that the ESG indices show significant forecasting power for crude oil returns both in- and out-of-sample, and their ability to significantly predict oil returns remains when the delayed ESG release is considered. Additionally, our analysis suggests that the predictive abilities of ESG indices remain robust and unaffected by stock returns in the oil-related industry. The ESG indices can provide information that is heterogeneous and complementary to macroeconomic variables and technical indicators. Based on the analysis over the business cycle, ESG indices show predictability in forecasting crude oil returns during economic expansions rather than recessions. Moreover, ESG indices' predictive ability is also of economic significance, as shown by the substantial economic value it generates for mean-variance investors. Finally, we explore the potential economic channels, and the result reveals that the predictive power of ESG indices arises from speculative behavior in the oil market and oil demand.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100444"},"PeriodicalIF":3.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577461","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 : 2024-10-26DOI: 10.1016/j.jcomm.2024.100443
Yanli Zhu , Xian Yang , Chuanhai Zhang , Sihan Liu , Jiayi Li
This paper investigates the role of infectious disease uncertainty on multi-scale risk spillovers and portfolio implications across 12 international commodity futures markets from January 2006 to August 2022. We use wavelet packet decomposition and a novel risk spillover network topology approach based on a smooth transition vector autoregression model. The main findings are summarized as follows. First, there is an obvious asymmetry in spillover effects, i.e., the intensity of risk spillovers increases significantly during periods of high infectious disease uncertainty, and clear evidence of time-varying total spillovers across various regimes and frequencies. Second, cross-category risk spillovers are more pronounced in high-uncertainty regimes, while risk networks tend to cluster within the same category during low-uncertainty regimes. Third, the role of commodity futures in the risk spillover networks varies across different time scales and regimes, with gold consistently acting as a stable net risk transmitter. We also develop optimal portfolio strategies across commodity futures markets at different time scales and regimes based on the risk spillover analysis.
{"title":"Asymmetric multi-scale systemic risk spillovers across international commodity futures markets: The role of infectious disease uncertainty","authors":"Yanli Zhu , Xian Yang , Chuanhai Zhang , Sihan Liu , Jiayi Li","doi":"10.1016/j.jcomm.2024.100443","DOIUrl":"10.1016/j.jcomm.2024.100443","url":null,"abstract":"<div><div>This paper investigates the role of infectious disease uncertainty on multi-scale risk spillovers and portfolio implications across 12 international commodity futures markets from January 2006 to August 2022. We use wavelet packet decomposition and a novel risk spillover network topology approach based on a smooth transition vector autoregression model. The main findings are summarized as follows. First, there is an obvious asymmetry in spillover effects, i.e., the intensity of risk spillovers increases significantly during periods of high infectious disease uncertainty, and clear evidence of time-varying total spillovers across various regimes and frequencies. Second, cross-category risk spillovers are more pronounced in high-uncertainty regimes, while risk networks tend to cluster within the same category during low-uncertainty regimes. Third, the role of commodity futures in the risk spillover networks varies across different time scales and regimes, with gold consistently acting as a stable net risk transmitter. We also develop optimal portfolio strategies across commodity futures markets at different time scales and regimes based on the risk spillover analysis.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100443"},"PeriodicalIF":3.7,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572506","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 : 2024-10-20DOI: 10.1016/j.jcomm.2024.100441
Linjie Wang , Xiaoli Etienne , Jian Li
This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.
{"title":"Food-fuel nexus beyond mean-variance: New evidence from a quantile approach","authors":"Linjie Wang , Xiaoli Etienne , Jian Li","doi":"10.1016/j.jcomm.2024.100441","DOIUrl":"10.1016/j.jcomm.2024.100441","url":null,"abstract":"<div><div>This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.</div></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"36 ","pages":"Article 100441"},"PeriodicalIF":3.7,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536028","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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