Pub Date : 2024-09-01Epub Date: 2024-06-22DOI: 10.1016/j.jcomm.2024.100419
Liam Welsh , Sebastian Jaimungal
One approach to reducing greenhouse gas (GHG) emissions is to incentivise carbon capturing and carbon reducing projects while simultaneously penalising excess GHG output. In this work, we present a novel market framework and characterise the optimal behaviour of GHG offset credit (OC) market participants in both single-player and two-player settings. The single player setting is posed as an optimal stopping and control problem, while the two-player setting is posed as optimal stopping and mixed-Nash equilibria problem. We demonstrate the importance of acting optimally using numerical solutions and Monte Carlo simulations and explore the differences between the homogeneous and heterogeneous players. In both settings, we find that market participants benefit from optimal OC trading and OC generation.
{"title":"Nash equilibria in greenhouse gas offset credit markets","authors":"Liam Welsh , Sebastian Jaimungal","doi":"10.1016/j.jcomm.2024.100419","DOIUrl":"https://doi.org/10.1016/j.jcomm.2024.100419","url":null,"abstract":"<div><p>One approach to reducing greenhouse gas (GHG) emissions is to incentivise carbon capturing and carbon reducing projects while simultaneously penalising excess GHG output. In this work, we present a novel market framework and characterise the optimal behaviour of GHG offset credit (OC) market participants in both single-player and two-player settings. The single player setting is posed as an optimal stopping and control problem, while the two-player setting is posed as optimal stopping and mixed-Nash equilibria problem. We demonstrate the importance of acting optimally using numerical solutions and Monte Carlo simulations and explore the differences between the homogeneous and heterogeneous players. In both settings, we find that market participants benefit from optimal OC trading and OC generation.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"35 ","pages":"Article 100419"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405851324000382/pdfft?md5=9ee3dcec23d33e3668b5de6e7d603887&pid=1-s2.0-S2405851324000382-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540786","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}
Pub Date : 2024-09-01Epub Date: 2024-08-06DOI: 10.1016/j.jcomm.2024.100422
Colin A. Carter , Sandro Steinbach
We study the impact of the 2022 Russian invasion of Ukraine on grain futures prices. The war allows us to evaluate whether commodity futures markets at the time were driven by investor herding. Using event study methods, we find that wheat futures prices rose by 35 percent above the counterfactual until the EU Solidarity Lanes were announced, more than corn futures prices, which were up 16 percent. This relative price response cannot be explained by herding behavior. Furthermore, prices for control commodities did not respond to the war at all, contradicting the herding theory. There is no statistical evidence of abnormal speculative pressure in the market around the time of the invasion, and we conclude the markets put a fair price on the wartime risk of Black Sea grain shipment disruptions.
{"title":"Did grain futures prices overreact to the Russia–Ukraine war due to herding?","authors":"Colin A. Carter , Sandro Steinbach","doi":"10.1016/j.jcomm.2024.100422","DOIUrl":"10.1016/j.jcomm.2024.100422","url":null,"abstract":"<div><p>We study the impact of the 2022 Russian invasion of Ukraine on grain futures prices. The war allows us to evaluate whether commodity futures markets at the time were driven by investor herding. Using event study methods, we find that wheat futures prices rose by 35 percent above the counterfactual until the EU Solidarity Lanes were announced, more than corn futures prices, which were up 16 percent. This relative price response cannot be explained by herding behavior. Furthermore, prices for control commodities did not respond to the war at all, contradicting the herding theory. There is no statistical evidence of abnormal speculative pressure in the market around the time of the invasion, and we conclude the markets put a fair price on the wartime risk of Black Sea grain shipment disruptions.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"35 ","pages":"Article 100422"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405851324000412/pdfft?md5=fc62b6ef0b705f197ec6895a5dbb53f4&pid=1-s2.0-S2405851324000412-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985590","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}
Pub Date : 2024-09-01Epub Date: 2024-06-18DOI: 10.1016/j.jcomm.2024.100420
Hyun-Tak Lee , Heesung Yun
This study examines the relationship between the unexpected changes in earnings and the shipping market movements. The econometric method of variance decomposition proposed by Campbell (1991) is employed to empirically analyze the Panamax and Capesize markets. We find that a large proportion of unexpected earnings growth is related to news about returns that indicate subsequent price changes. The results provide important insights to practice for sustaining shipping businesses, which helps shipping companies make better investment and risk-management decisions. The contribution of this research is to deepen the understanding of the interaction between shocks to earnings growth, returns, and price–charter ratios in the present-value context.
{"title":"Understanding the variance of earnings growth: The case of shipping","authors":"Hyun-Tak Lee , Heesung Yun","doi":"10.1016/j.jcomm.2024.100420","DOIUrl":"https://doi.org/10.1016/j.jcomm.2024.100420","url":null,"abstract":"<div><p>This study examines the relationship between the unexpected changes in earnings and the shipping market movements. The econometric method of variance decomposition proposed by Campbell (1991) is employed to empirically analyze the Panamax and Capesize markets. We find that a large proportion of unexpected earnings growth is related to news about returns that indicate subsequent price changes. The results provide important insights to practice for sustaining shipping businesses, which helps shipping companies make better investment and risk-management decisions. The contribution of this research is to deepen the understanding of the interaction between shocks to earnings growth, returns, and price–charter ratios in the present-value context.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"35 ","pages":"Article 100420"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141439122","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 : 2024-09-01Epub Date: 2024-07-25DOI: 10.1016/j.jcomm.2024.100421
Johannes Mauritzen
The role of financing in petroleum exploration has gained prominence due to sustainability commitments by major financing institutions. Yet the relationship between exploration and financing has been little explored and poorly understood. I create a novel data set combining detailed exploration data with financial register data on all public and private firms operating on the Norwegian Continental Shelf to analyze the relationship between debt and drilling decisions. I make use of both an over-dispersed Poisson regression model estimated by maximum likelihood and a Bayesian hierarchical negative binomial regression model where key elements of the industry microstructure are specified and explicitly modeled. I find evidence that short-term debt is associated with lower rates of drilling and more modest evidence that long-term debt has a slightly positive relationship with exploratory drilling. This evidence is consistent with a financial constraints theory of oil drilling, and supports the argument that exploration drilling is dependent on a firms access to financing.
{"title":"Boring finance. Petroleum exploration and firm debt: Evidence from Norway","authors":"Johannes Mauritzen","doi":"10.1016/j.jcomm.2024.100421","DOIUrl":"10.1016/j.jcomm.2024.100421","url":null,"abstract":"<div><p>The role of financing in petroleum exploration has gained prominence due to sustainability commitments by major financing institutions. Yet the relationship between exploration and financing has been little explored and poorly understood. I create a novel data set combining detailed exploration data with financial register data on all public and private firms operating on the Norwegian Continental Shelf to analyze the relationship between debt and drilling decisions. I make use of both an over-dispersed Poisson regression model estimated by maximum likelihood and a Bayesian hierarchical negative binomial regression model where key elements of the industry microstructure are specified and explicitly modeled. I find evidence that short-term debt is associated with lower rates of drilling and more modest evidence that long-term debt has a slightly positive relationship with exploratory drilling. This evidence is consistent with a financial constraints theory of oil drilling, and supports the argument that exploration drilling is dependent on a firms access to financing.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"35 ","pages":"Article 100421"},"PeriodicalIF":3.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405851324000400/pdfft?md5=29d0c96789b36b778c1a715a6442ff8f&pid=1-s2.0-S2405851324000400-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850984","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}
Pub Date : 2024-06-01Epub Date: 2024-02-13DOI: 10.1016/j.jcomm.2024.100388
Yanqiong Liu , Yaoqi Guo , Qing Wei
In this paper, we select 75 indicators to conduct a comprehensive analysis of the factors influencing the copper price along six dimensions: inventory, supply, demand, the macroeconomy, finance, and geopolitics. Facing the high-dimensionality problem, we use the least absolute shrinkage and selection operator (LASSO) regression model to select variables to measure the contribution of each category of factors. Furthermore, we identify the time-varying nature of the relationship among factors with rolling windows. Then, we decompose copper prices into different scales of fluctuations by means of empirical mode decomposition (EMD) and investigate the driving factors at each scale. The results show that financial and geopolitical factors have played an important role in copper pricing in recent years. The long-run fluctuation trend of copper prices is mainly determined by fundamental factors, while financial and geopolitical factors have a more direct impact on short-term fluctuations.
{"title":"Time-varying and multi-scale analysis of copper price influencing factors based on LASSO and EMD methods","authors":"Yanqiong Liu , Yaoqi Guo , Qing Wei","doi":"10.1016/j.jcomm.2024.100388","DOIUrl":"10.1016/j.jcomm.2024.100388","url":null,"abstract":"<div><p>In this paper, we select 75 indicators to conduct a comprehensive analysis of the factors influencing the copper price along six dimensions: inventory, supply, demand, the macroeconomy, finance, and geopolitics. Facing the high-dimensionality problem, we use the least absolute shrinkage and selection operator (LASSO) regression model to select variables to measure the contribution of each category of factors. Furthermore, we identify the time-varying nature of the relationship among factors with rolling windows. Then, we decompose copper prices into different scales of fluctuations by means of empirical mode decomposition (EMD) and investigate the driving factors at each scale. The results show that financial and geopolitical factors have played an important role in copper pricing in recent years. The long-run fluctuation trend of copper prices is mainly determined by fundamental factors, while financial and geopolitical factors have a more direct impact on short-term fluctuations.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100388"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139829019","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 : 2024-06-01Epub Date: 2024-02-05DOI: 10.1016/j.jcomm.2024.100387
Priti Biswas , Prachi Jain , Debasish Maitra
We study the immediate impact of heightened geopolitical tensions caused by the Russia-Ukraine war, on volatility connectedness networks of 18 global stock markets and 5 major commodities. Our analyses reveal a shift in connectedness spillovers during the war: while crude oil (a net shock transmitter before the war) became a net shock receiver, shocks transmitted by crude oil net importers appear to primarily contribute to crude oil turning a net shock receiver, whereas for platinum and wheat, we observe that both net exporters and importers have received volatility shocks. We further dissect the impact of war on the direction of spillovers using panel censored regressions. Employing insights from the analyses, we design portfolios that weigh higher (lower) on stock indices with lower (higher) pairwise connectedness (PCI) to each commodity. We not only find these PCI-based portfolios to exhibit safe-haven properties under extreme geopolitical risk, but they also outperform an equally-weighted portfolio during a period of war. Finally, low-minus-high factors constructed on pairwise connectedness have significant explanatory power for portfolio returns, indicating connectedness as an additional factor for asset pricing models.
{"title":"Are shocks in the stock markets driven by commodity markets? Evidence from Russia-Ukraine war","authors":"Priti Biswas , Prachi Jain , Debasish Maitra","doi":"10.1016/j.jcomm.2024.100387","DOIUrl":"10.1016/j.jcomm.2024.100387","url":null,"abstract":"<div><p>We study the immediate impact of heightened geopolitical tensions caused by the Russia-Ukraine war, on volatility connectedness networks of 18 global stock markets and 5 major commodities. Our analyses reveal a shift in connectedness spillovers during the war: while crude oil (a net shock transmitter before the war) became a net shock receiver, shocks transmitted by crude oil net importers appear to primarily contribute to crude oil turning a net shock receiver, whereas for platinum and wheat, we observe that both net exporters and importers have received volatility shocks. We further dissect the impact of war on the direction of spillovers using panel censored regressions. Employing insights from the analyses, we design portfolios that weigh higher (lower) on stock indices with lower (higher) pairwise connectedness (PCI) to each commodity. We not only find these PCI-based portfolios to exhibit safe-haven properties under extreme geopolitical risk, but they also outperform an equally-weighted portfolio during a period of war. Finally, low-minus-high factors constructed on pairwise connectedness have significant explanatory power for portfolio returns, indicating connectedness as an additional factor for asset pricing models.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100387"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765475","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 : 2024-06-01Epub Date: 2024-05-10DOI: 10.1016/j.jcomm.2024.100406
Dirk G. Baur, Jonathan R. Karlsen, Lee A. Smales, Allan Trench
Bitcoin is often labelled digital gold and many studies compare bitcoin and gold prices, returns and volatility. This paper digs deeper and compares the characteristics of bitcoin mining with gold mining to assess claims that bitcoin is digital gold. We identify 20 differences between gold and bitcoin mining. Gold miners locate where gold is present while bitcoin miners locate where cheap electricity is present. Gold mining has large barriers to entry relative to bitcoin mining making it relatively difficult to start and abandon a gold mine but much easier to start and abandon a bitcoin mine. This is reflected in a greater exposure of gold miners to gold prices and a smaller exposure of bitcoin miners to bitcoin prices. While the analysis demonstrates that bitcoin mining is less complex and less risky than gold mining, the similarities support the idea that bitcoin is digital gold.
{"title":"Digging deeper - Is bitcoin digital gold? A mining perspective","authors":"Dirk G. Baur, Jonathan R. Karlsen, Lee A. Smales, Allan Trench","doi":"10.1016/j.jcomm.2024.100406","DOIUrl":"https://doi.org/10.1016/j.jcomm.2024.100406","url":null,"abstract":"<div><p>Bitcoin is often labelled digital gold and many studies compare bitcoin and gold prices, returns and volatility. This paper digs deeper and compares the characteristics of bitcoin mining with gold mining to assess claims that bitcoin is digital gold. We identify 20 differences between gold and bitcoin mining. Gold miners locate where gold is present while bitcoin miners locate where cheap electricity is present. Gold mining has large barriers to entry relative to bitcoin mining making it relatively difficult to start and abandon a gold mine but much easier to start and abandon a bitcoin mine. This is reflected in a greater exposure of gold miners to gold prices and a smaller exposure of bitcoin miners to bitcoin prices. While the analysis demonstrates that bitcoin mining is less complex and less risky than gold mining, the similarities support the idea that bitcoin is digital gold.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100406"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140924456","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 : 2024-06-01Epub Date: 2024-02-15DOI: 10.1016/j.jcomm.2024.100389
Michel A. Robe , John S. Roberts
The CFTC’s Commitments of Traders reports (DCOT and SCOT) are a key source of information about the open interest in commodity derivatives markets. While informative, these publications leave open four important questions. (1) Do traders that hold large positions every single day make up most of the total open interest? How big is that “market core”? (2) What is the relation between DCOT figures on swap dealer futures positions and CIT futures positions? (3) Are most futures traders long-only or short-only, or do they hold “mixed” positions? (4) Who makes up the fast-growing “Other Reportables” category that comprises all noncommercial market participants that are not managed money traders? We tackle those questions with regulatory data on futures positions in the four largest U.S. grain and oilseed markets in 2015–2018.
{"title":"Four Commitments of Traders Reports puzzles, revisited: Answers from grains and oilseeds futures markets","authors":"Michel A. Robe , John S. Roberts","doi":"10.1016/j.jcomm.2024.100389","DOIUrl":"10.1016/j.jcomm.2024.100389","url":null,"abstract":"<div><p>The CFTC’s Commitments of Traders reports (DCOT and SCOT) are a key source of information about the open interest in commodity derivatives markets. While informative, these publications leave open four important questions. (1) Do traders that hold large positions every single day make up most of the total open interest? How big is that “market core”? (2) What is the relation between DCOT figures on swap dealer futures positions and CIT futures positions? (3) Are most futures traders long-only or short-only, or do they hold “mixed” positions? (4) Who makes up the fast-growing “Other Reportables” category that comprises all noncommercial market participants that are not managed money traders? We tackle those questions with regulatory data on futures positions in the four largest U.S. grain and oilseed markets in 2015–2018.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100389"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824692","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 : 2024-06-01Epub Date: 2024-05-15DOI: 10.1016/j.jcomm.2024.100407
Linh Pham , Javed Bin Kamal
In this paper, we examine the time-varying tail risks transmission among the agricultural, precious metals, and energy commodities markets, and explore how climate change concerns affect this connectedness. Using the Conditional Autoregressive Value-at-Risk (CAViaR) model and the time-varying parameter vector autoregressive (TVP-VAR) connectedness model, our empirical analysis reveals several key findings. First, our tail risk-based approach shows that tail risks transmission rises during crisis periods such as the GFC of 2007 and the Covid period of 2020. Second, climate risks, in particular climate transitions risks, play an important role in commodity tail risk connectedness. These findings are important for investors, practitioners, and policymakers. Our results are robust to a number of robustness tests.
{"title":"Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?","authors":"Linh Pham , Javed Bin Kamal","doi":"10.1016/j.jcomm.2024.100407","DOIUrl":"10.1016/j.jcomm.2024.100407","url":null,"abstract":"<div><p>In this paper, we examine the time-varying tail risks transmission among the agricultural, precious metals, and energy commodities markets, and explore how climate change concerns affect this connectedness. Using the Conditional Autoregressive Value-at-Risk (CAViaR) model and the time-varying parameter vector autoregressive (TVP-VAR) connectedness model, our empirical analysis reveals several key findings. First, our tail risk-based approach shows that tail risks transmission rises during crisis periods such as the GFC of 2007 and the Covid period of 2020. Second, climate risks, in particular climate transitions risks, play an important role in commodity tail risk connectedness. These findings are important for investors, practitioners, and policymakers. Our results are robust to a number of robustness tests.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100407"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054002","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 : 2024-06-01Epub Date: 2024-05-06DOI: 10.1016/j.jcomm.2024.100405
Christopher B. Burns , Daniel L. Prager
Using proprietary data reported by swap dealers to the Commodity Futures Trading Commission, we first present new evidence on the size and composition of 13 over-the-counter agricultural swaps markets. We then utilize our novel dataset to show the existence of linkages with the equity markets. We use the spike in the Chicago Board Options Exchange Volatility Index in early 2020 to show that swaps trader positions were significantly impacted by the financial market volatility created by the COVID-19 pandemic. Following similar methods as Cheng et al. (2015), we find index swaps traders reduce their net long positions in response to tightening financial conditions, while commercial swaps traders absorb some of this risk by decreasing their net short positions. This internal swap market netting occurs in three of the four largest agricultural markets: corn, soft red winter wheat, and sugar. Concurrently, we observe a limited swap dealer hedging response in the futures market, especially when compared to other financial traders, consistent with swap market netting. Our results confirm that equity market shocks can affect financial traders in both commodity swaps and futures markets.
{"title":"Do agricultural swaps co-move with equity markets? Evidence from the COVID-19 crisis","authors":"Christopher B. Burns , Daniel L. Prager","doi":"10.1016/j.jcomm.2024.100405","DOIUrl":"https://doi.org/10.1016/j.jcomm.2024.100405","url":null,"abstract":"<div><p>Using proprietary data reported by swap dealers to the Commodity Futures Trading Commission, we first present new evidence on the size and composition of 13 over-the-counter agricultural swaps markets. We then utilize our novel dataset to show the existence of linkages with the equity markets. We use the spike in the Chicago Board Options Exchange Volatility Index in early 2020 to show that swaps trader positions were significantly impacted by the financial market volatility created by the COVID-19 pandemic. Following similar methods as Cheng et al. (2015), we find index swaps traders reduce their net long positions in response to tightening financial conditions, while commercial swaps traders absorb some of this risk by decreasing their net short positions. This internal swap market netting occurs in three of the four largest agricultural markets: corn, soft red winter wheat, and sugar. Concurrently, we observe a limited swap dealer hedging response in the futures market, especially when compared to other financial traders, consistent with swap market netting. Our results confirm that equity market shocks can affect financial traders in both commodity swaps and futures markets.</p></div>","PeriodicalId":45111,"journal":{"name":"Journal of Commodity Markets","volume":"34 ","pages":"Article 100405"},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140948822","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}