We study the equilibrium relationship between the WTI and the Brent crude oil indexes in prices and in option-implied moments using fractional cointegration models from 2008-2016. This period has been subject to changing constraints in terms of rising US inventories and falling demand. Our results suggest there exists a cointegrating relationship in prices as well as between risk-neutral moments. While a long-lasting spread in prices is not supported by the data, our results support a significant volatility differential between the two oil indexes. The Cushing bottleneck is linked to slower speeds of adjustment to disequilibrium for both indexes as well as a fragmentation of the international equilibrium for tail and crash risk, especially for longer horizons. Crash and tail risk are more locally driven and less affected by the international equilibrium than are price and volatility.
{"title":"International Oil Market Risk Anticipations and the Cushing Bottleneck: Option-Implied Evidence","authors":"Marie‐Hélène Gagnon, G. Power","doi":"10.2139/ssrn.2840958","DOIUrl":"https://doi.org/10.2139/ssrn.2840958","url":null,"abstract":"We study the equilibrium relationship between the WTI and the Brent crude oil indexes in prices and in option-implied moments using fractional cointegration models from 2008-2016. This period has been subject to changing constraints in terms of rising US inventories and falling demand. Our results suggest there exists a cointegrating relationship in prices as well as between risk-neutral moments. While a long-lasting spread in prices is not supported by the data, our results support a significant volatility differential between the two oil indexes. The Cushing bottleneck is linked to slower speeds of adjustment to disequilibrium for both indexes as well as a fragmentation of the international equilibrium for tail and crash risk, especially for longer horizons. Crash and tail risk are more locally driven and less affected by the international equilibrium than are price and volatility.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Batten, Harald Kinateder, P. Szilagyi, N. Wagner
The relationship between energy and stock prices is investigated in the context of Asia, including China and Japan. Oil, gas and coal prices are considered both individually and in an energy portfolio. Consistent with evidence from analysis of other asset prices in international markets, during the post Global Financial Crisis (GFC) period, Asian stock markets moved in tandem with oil prices. However, using asset pricing and portfolio theory we identify time-varying integration between individual stock markets and the energy portfolio, which in turn may limit the benefit of risk reduction through diversification. However, this relation can also be used to hedge the common factor arising from energy risk. Doing so provides benefits to investors in the form of positive risk adjusted returns, although these are episodic.
{"title":"Can Investors Hedge Energy Risk? Evidence from Asia","authors":"J. Batten, Harald Kinateder, P. Szilagyi, N. Wagner","doi":"10.2139/ssrn.2962249","DOIUrl":"https://doi.org/10.2139/ssrn.2962249","url":null,"abstract":"The relationship between energy and stock prices is investigated in the context of Asia, including China and Japan. Oil, gas and coal prices are considered both individually and in an energy portfolio. Consistent with evidence from analysis of other asset prices in international markets, during the post Global Financial Crisis (GFC) period, Asian stock markets moved in tandem with oil prices. However, using asset pricing and portfolio theory we identify time-varying integration between individual stock markets and the energy portfolio, which in turn may limit the benefit of risk reduction through diversification. However, this relation can also be used to hedge the common factor arising from energy risk. Doing so provides benefits to investors in the form of positive risk adjusted returns, although these are episodic.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131558884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy that captures persistent anomalies in long-run relationships among assets. We devise a methodology to identify and test mean-reverting statistical arbitrage, and to develop trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium, and mispricings arise when West Texas Intermediate crude oil price diverges from the statistical portfolio value. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is measured by three specific profit indicators on out-of-sample data. Lastly, we use a Monte Carlo simulation approach to develop a model for forecasting the expected Value at Risk of the adopted trading strategy over an established holding period.
{"title":"Mean-Reverting Statistical Arbitrage in Crude Oil Markets","authors":"Viviana Fanelli","doi":"10.2139/ssrn.2949716","DOIUrl":"https://doi.org/10.2139/ssrn.2949716","url":null,"abstract":"In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy that captures persistent anomalies in long-run relationships among assets. We devise a methodology to identify and test mean-reverting statistical arbitrage, and to develop trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium, and mispricings arise when West Texas Intermediate crude oil price diverges from the statistical portfolio value. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is measured by three specific profit indicators on out-of-sample data. Lastly, we use a Monte Carlo simulation approach to develop a model for forecasting the expected Value at Risk of the adopted trading strategy over an established holding period.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131814382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christos Alexakis, Guillaume Bagnarosa, M. Dowling
Hog, corn, and soybean meal futures are shown to be cointegrated, reflecting the close intrinsic relationship of corn and soybean meal as the primary feed for hogs. Applying a recent technique to date-stamp pricing bubbles we further show that bubbles in feed do not appear to be associated with bubbles in the price of hogs. Instead there are temporary deviations in the spread between hog and feed, but the long-term cointegration relationship leads to a reversion towards the common trend. This finding sheds new insight into the price behaviour of commodities that depend for input costs on other commodities.
{"title":"Do Cointegrated Commodities Bubble Together? The Case of Hog, Corn, and Soybean","authors":"Christos Alexakis, Guillaume Bagnarosa, M. Dowling","doi":"10.2139/ssrn.2914152","DOIUrl":"https://doi.org/10.2139/ssrn.2914152","url":null,"abstract":"Hog, corn, and soybean meal futures are shown to be cointegrated, reflecting the close intrinsic relationship of corn and soybean meal as the primary feed for hogs. Applying a recent technique to date-stamp pricing bubbles we further show that bubbles in feed do not appear to be associated with bubbles in the price of hogs. Instead there are temporary deviations in the spread between hog and feed, but the long-term cointegration relationship leads to a reversion towards the common trend. This finding sheds new insight into the price behaviour of commodities that depend for input costs on other commodities.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129412773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a continuous-time model for commodity pricing under the assumption that logged prices converge to a mean level that experiences smooth, periodic fluctuations over long periods of time. Our model incorporates that assumption by modelling the mean reversion level through a Fourier series. To validate the model, we perform an empirical study of futures prices on Natural Gas, Crude Oil, and Heating Oil. We provide evidence that such long-term fluctuations are present in the price of these energy commodities, possibly together with standard seasonal and cyclical components. We analyse the empirical performance of our pricing model versus two alternative competitors, namely, those proposed in Schwartz (1997) and Lucia and Schwartz (2002). Our findings show that our model outperforms both benchmarks, providing a simple and powerful tool for portfolio management, risk management and derivative pricing.
{"title":"Long-Term Swings and Seasonality in Energy Markets","authors":"Manuel Moreno, A. Novales, Federico Platania","doi":"10.2139/ssrn.2788176","DOIUrl":"https://doi.org/10.2139/ssrn.2788176","url":null,"abstract":"This paper introduces a continuous-time model for commodity pricing under the assumption that logged prices converge to a mean level that experiences smooth, periodic fluctuations over long periods of time. Our model incorporates that assumption by modelling the mean reversion level through a Fourier series. To validate the model, we perform an empirical study of futures prices on Natural Gas, Crude Oil, and Heating Oil. We provide evidence that such long-term fluctuations are present in the price of these energy commodities, possibly together with standard seasonal and cyclical components. We analyse the empirical performance of our pricing model versus two alternative competitors, namely, those proposed in Schwartz (1997) and Lucia and Schwartz (2002). Our findings show that our model outperforms both benchmarks, providing a simple and powerful tool for portfolio management, risk management and derivative pricing.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130375560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we investigate supply and demand shocks in the US natural gas market, focusing on how the effects of these shocks have changed over time. Using a sign-identified structural vector autoregression (SVAR) model that allows for both time-varying parameters and stochastic volatility, we decompose the real price of natural gas into supply shocks, aggregate demand shocks driven by changes in the US economic activity, precautionary inventory demand in anticipation of changes in future demand-and-supply conditions, and residual demand shocks not otherwise accounted for by the previous three shocks. Using quarterly data from 1976 to 2015, we find that an unanticipated supply disruption raises natural gas prices, reduces the aggregate economic demand, and lowers the precautionary inventory demand, while negative aggregate demand shocks, on the other hand, depress natural gas prices, reduce natural gas production, and increase precautionary inventory demand. We also find that following a negative precautionary inventory demand shock, aggregate demand driven by real economic activity declines marginally, and the marketed natural gas production and real prices decrease as well. Our results further suggest that such impact responses have evolved considerably over time with changing market conditions.
{"title":"Turbulent Times: Uncovering the Origins of US Natural Gas Price Fluctuations Since 1993","authors":"S. Wiggins, Xiaoli L. Etienne","doi":"10.2139/ssrn.2738089","DOIUrl":"https://doi.org/10.2139/ssrn.2738089","url":null,"abstract":"In this paper, we investigate supply and demand shocks in the US natural gas market, focusing on how the effects of these shocks have changed over time. Using a sign-identified structural vector autoregression (SVAR) model that allows for both time-varying parameters and stochastic volatility, we decompose the real price of natural gas into supply shocks, aggregate demand shocks driven by changes in the US economic activity, precautionary inventory demand in anticipation of changes in future demand-and-supply conditions, and residual demand shocks not otherwise accounted for by the previous three shocks. Using quarterly data from 1976 to 2015, we find that an unanticipated supply disruption raises natural gas prices, reduces the aggregate economic demand, and lowers the precautionary inventory demand, while negative aggregate demand shocks, on the other hand, depress natural gas prices, reduce natural gas production, and increase precautionary inventory demand. We also find that following a negative precautionary inventory demand shock, aggregate demand driven by real economic activity declines marginally, and the marketed natural gas production and real prices decrease as well. Our results further suggest that such impact responses have evolved considerably over time with changing market conditions.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124710086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It has been forty years since the oil crisis of 1973/74. This crisis has been one of the defining economic events of the 1970s and has shaped how many economists think about oil price shocks. In recent years, a large literature on the economic determinants of oil price fluctuations has emerged. Drawing on this literature, we first provide an overview of the causes of all major oil price fluctuations between 1973 and 2014. We then discuss why oil price fluctuations remain difficult to predict, despite economists’ improved understanding of oil markets. Unexpected oil price fluctuations are commonly referred to as oil price shocks. We document that, in practice, consumers, policymakers, financial market participants and economists may have different oil price expectations, and that, what may be surprising to some, need not be equally surprising to others.
{"title":"Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us","authors":"C. Baumeister, L. Kilian","doi":"10.2139/ssrn.2714319","DOIUrl":"https://doi.org/10.2139/ssrn.2714319","url":null,"abstract":"It has been forty years since the oil crisis of 1973/74. This crisis has been one of the defining economic events of the 1970s and has shaped how many economists think about oil price shocks. In recent years, a large literature on the economic determinants of oil price fluctuations has emerged. Drawing on this literature, we first provide an overview of the causes of all major oil price fluctuations between 1973 and 2014. We then discuss why oil price fluctuations remain difficult to predict, despite economists’ improved understanding of oil markets. Unexpected oil price fluctuations are commonly referred to as oil price shocks. We document that, in practice, consumers, policymakers, financial market participants and economists may have different oil price expectations, and that, what may be surprising to some, need not be equally surprising to others.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121877709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We review how China has become a dominant influence in global commodity markets due to the economy’s size and commodity intensity. We then focus on the emergence of China’s credit market as a new influence on commodity prices using a vector autoregression model and recursive identification. We find that a 1 percentage point (ppt) surprise increase in China’s bank lending results in statistically significant price increases of 10-12 percent for some base metals, including copper. This contrasts with a 1 ppt shock to China’s industrial production which leads to a statistically significant change of 7-9 percent of aluminum, copper, and crude oil. We suggest that one reason for the large influence of China’s credit aggregates may be the important role that some commodities play as collateral for lending in a financial system still bedeviled by information asymmetries, particularly for private sector borrowers.
{"title":"China: Credit, Collateral, and Commodity Prices","authors":"S. Roache, M. Rousset","doi":"10.2139/ssrn.2709295","DOIUrl":"https://doi.org/10.2139/ssrn.2709295","url":null,"abstract":"We review how China has become a dominant influence in global commodity markets due to the economy’s size and commodity intensity. We then focus on the emergence of China’s credit market as a new influence on commodity prices using a vector autoregression model and recursive identification. We find that a 1 percentage point (ppt) surprise increase in China’s bank lending results in statistically significant price increases of 10-12 percent for some base metals, including copper. This contrasts with a 1 ppt shock to China’s industrial production which leads to a statistically significant change of 7-9 percent of aluminum, copper, and crude oil. We suggest that one reason for the large influence of China’s credit aggregates may be the important role that some commodities play as collateral for lending in a financial system still bedeviled by information asymmetries, particularly for private sector borrowers.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoli L. Etienne, Andres A. Trujillo‐Barrera, S. Wiggins
Purpose - – The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance. Design/methodology/approach - – The authors jointly estimate a vector error correction model for the conditional mean equation and a multivariate generalized autoregressive heteroskedasticity model for the conditional volatility equation to investigate the interactions between natural gas, ammonia, and corn prices and their volatility. Findings - – The authors find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short run, but both prices react to deviations from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong. Originality/value - – This paper is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.
{"title":"Price and Volatility Transmissions between Natural Gas, Fertilizer, and Corn Markets","authors":"Xiaoli L. Etienne, Andres A. Trujillo‐Barrera, S. Wiggins","doi":"10.2139/ssrn.2684047","DOIUrl":"https://doi.org/10.2139/ssrn.2684047","url":null,"abstract":"Purpose - – The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance. Design/methodology/approach - – The authors jointly estimate a vector error correction model for the conditional mean equation and a multivariate generalized autoregressive heteroskedasticity model for the conditional volatility equation to investigate the interactions between natural gas, ammonia, and corn prices and their volatility. Findings - – The authors find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short run, but both prices react to deviations from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong. Originality/value - – This paper is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124197220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper studies the optimal component procurement strategies of two competing OEMs selling substitutable products. The OEMs outsource their production to a common contract manufacturer, who in turn needs an input from a component supplier. Each OEM may either directly procure the input from the component supplier, or delegate the procurement task to the contract manufacturer. We first analyze the OEMs' procurement game under a non-strategic supplier whose component price is exogenously given. It is found that symmetric equilibria arise for most situations, i.e., both OEMs either control or delegate their component procurement in equilibrium. Interestingly, despite the commonly-held belief that the contract manufacturer would be worse off as OEMs gain component procurement control, we show that the contract manufacturer may enjoy a higher profit. Then we study the OEMs' procurement game under a strategic supplier who can set its component price. We find that the supplier's strategic pricing behavior plays a critical role in the equilibrium procurement structure. In particular, in the equilibrium under strategic supplier, the larger OEM always uses delegation while the smaller OEM may use either delegation or control. By identifying the driving forces behind the OEMs' procurement choices, this research helps explain observed industry practices and offer useful guidelines for firms' component sourcing decisions.
{"title":"Delegation versus Control in Supply Chain Procurement Under Competition","authors":"E. Bolandifar, Panos Kouvelis, Fuqiang Zhang","doi":"10.2139/ssrn.2661059","DOIUrl":"https://doi.org/10.2139/ssrn.2661059","url":null,"abstract":"This paper studies the optimal component procurement strategies of two competing OEMs selling substitutable products. The OEMs outsource their production to a common contract manufacturer, who in turn needs an input from a component supplier. Each OEM may either directly procure the input from the component supplier, or delegate the procurement task to the contract manufacturer. We first analyze the OEMs' procurement game under a non-strategic supplier whose component price is exogenously given. It is found that symmetric equilibria arise for most situations, i.e., both OEMs either control or delegate their component procurement in equilibrium. Interestingly, despite the commonly-held belief that the contract manufacturer would be worse off as OEMs gain component procurement control, we show that the contract manufacturer may enjoy a higher profit. Then we study the OEMs' procurement game under a strategic supplier who can set its component price. We find that the supplier's strategic pricing behavior plays a critical role in the equilibrium procurement structure. In particular, in the equilibrium under strategic supplier, the larger OEM always uses delegation while the smaller OEM may use either delegation or control. By identifying the driving forces behind the OEMs' procurement choices, this research helps explain observed industry practices and offer useful guidelines for firms' component sourcing decisions.","PeriodicalId":233145,"journal":{"name":"Global Commodity Issues (Editor's Choice) eJournal","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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