石油产品定价外文文献翻译中英文

石油产品定价外文文献翻译中英文
（节选重点翻译）
英文
Characteristics of petroleum product prices: A survey
Louis Ederington，Chitru Fernando，etc
Abstract
We review and synthesize the empirical evidence on several factors related to petroleum product prices: (1) the general distributional characteristics of petroleum product prices; (2) the influence of refinery outages, extreme weather, and similar circumstances on product prices; (3) the way that price discovery occurs for petroleum products; (4) the predictive accuracy of petroleum product futures prices for future spot prices; and (5) the impact of speculation on product prices.
Keywords: Petroleum product prices, Commodities, Energy economics
1. Introduction
This review is the second of a two-part series surveying the extant literature on the behavior and determinants of petroleum productfutures prices. Ederington et al. (2018a) focuses on the relation between petroleum product prices and oil prices. In this part, we turn to the general distributional characteristics of petroleum product prices, the influence of fundamental factors such as refinery outages and the weather on product
prices, the way that price discovery occurs for petroleum products, and the predictive accuracy of petroleum product futures prices for future spot prices.
Recent years have seen a surge in research devoted to the question of whether fundamental supply and demand factors or excess speculation have influenced movements in crude oil prices. However, little attention has been paid to whether speculation appears to influence petroleum product prices. We also review the research on this topic.
2. Prices and inventories
The theory of storage (Kaldor, 1939; Working, 1949) predicts that price volatility and price level are inversely related to inventory levels. When there are little or no inventories to act as a buffer, imbalances in supply and demand may result in dramatic price changes. In addition, prices and volatility will be positively correlated, as both are negatively related to inventories. A separate argument made by Smith (2009) and others emphasizes that oil price volatility can be high because the underlying demand and supplycurves are so price-inelastic that shocks to supply or demand are immediately reflected in the price. Finally, the relation between volatility and inventory can run in the opposite direction as well; that is, volatility can potentially influence inventory levels. For instance, as Pindyck (2004) has suggested, high oil price volatility increases the opportunity cost of producing now in contrast to producing
later; that is, waiting to see if future spot prices are greater than current spot prices. Balancing these views, Smith (2009) points out that price volatility provides incentives to hold inventories, but because inventories are costly, they may not be sufficiently large to fully offset the rigidity of demand and supply.
Gorton et al. (2012) study the relation between petroleum productfutures' excess returns (what they refer to as the risk premium) and inventories. They find that time-series variation and cross-sectional variation of the risk premium are inversely related to inventory levels of heating oil, gasoline, and crude oil. However, although negative relations are documented for all three commodities, none is statistically different from zero at conventional levels. The authors attribute the weak statistical significance levels to the fact that inventories are measured with error.
A recent study completed by the U.S. Energy Information Administration (U.S. Energy Information Administration, 2014) focuses on the relation between changes in the benchmark prices (Brent and WTI) and gasoline prices. The study also presents results regarding the conditional effects on gasoline prices of changes in deviations of gasoline inventories from prior five-year averages. The authors generally find an inverse and statistically significant relation between inventory deviations and gasoline price change.
Pindyck (2004) develops a structural model of inventories, spot
prices, and futures prices that explicitly considers the role of volatility and estimates the model with daily and weekly data on crude oil, heating oil, and gasoline during the period 1984–2001. Weekly volatilities are estimated as sample standard deviations of adjusted daily log changes in prices. Pindyck (2004) finds that spot prices, inventories, and convenience yield do not cause volatility in crude oil and thus concludes that volatility is an exogenous variable. However, he also finds that the model performs poorly for the crude oil market. For heating oil, changes in volatility influence convenience yields and, to a lesser extent, inventories, but the effects are not large. There is no strong evidence of such effects in the crude oil and gasoline markets. Furthermore, although changes in heating oil volatility can help explain changes in the spot-futures spread (convenience yield), it does not explain the spot price itself. Pindyck (2004) concludes that the results fit the theoretical predictions for heating oil but not for crude oil or gasoline. Pindyck conjectures that the mixed results might be an artifact of model misspecifications or that market variables affect production decisions more slowly than can be captured with weekly data. Pindyck also notes that speculation might influence price volatility, which is not considered in the model.
Kaufmann et al. (2009) study the relation between oil prices, gasoline prices, inventory levels for oil and gasoline, refinery utilization
rates, and the price of a substitute fuel (natural gas). They are interested in how oil price changes are transmitted horizontally and/or vertically through the energy supply chain. The authors define “horizontal transmissions as changes that are generated by linkages among fuels at a similar stage of processing while vertical transmissions are changes that are generated by upstream/downstream linkages in the oil supply chain” (p. 644). They estimate vector error correction models among the series using both weekly and quarterly observations, concluding from tests of causality that the price of crude oil is an important determinant of behaviors throughout the oil supply chain. Analyzing impulse response functions, they find that shocks to crude oil prices affect inventory behaviors, refinery utilization rates, and the price of motor gasoline, and are transmitted laterally to the natural gas market.
Studies by Kilian (2010) and Bilgin and Ellwanger (2017) both emphasize the role of inventories (both oil inventories and inventories of petroleum products) in the formation of gasoline demand and prices. Kilian (2010) examines the relationship between the global crude oil market and U.S. fuel consumption. He finds significant oil price effects associated with shifts in U.S. gasoline consumption. Bilgin and Ellwanger (2017) develop and estimate a model of the oil and gasoline market that reflects the underlying premise that “demand from crude oil is derived from the demand for oil products that it is converted to” (p. 3).
The model includes both oil and petroleum product inventories. The authors find that global fuel demand is inelastic with respect to crude oil prices.
3. Refinery outages, weather-related factors and product prices
Focusing on weekly wholesale gasoline prices in the United States from January 2002 to September 2008, Kendix and Walls (2010)quantify the impact of refinery outages on petroleum product prices. The authors match refinery unit output to specific wholesale gasoline markets, then estimate panel data regressions and quantify the impact of refinery unit outages on wholesale gasoline prices. They control for time-specific effects, city-specific effects, fuel-specific effects, refinery concentration, and other factors that could affect the prices of refined petroleum products. Their results show that refinery outages have a statistically significant positive impact on refined product prices and that the magnitude of this effect is larger for certain fuel blends.
Chesnes (2015) also investigates the relation between refinery outages on current petroleum product prices and future refinery investment. The author studies detailed refinery-level data on planned and unplanned refinery outages for the period 2001–2011. The data are measured at the monthly frequency, which includes the wholesale prices studied. Outages are aggregated to the Petroleum Administration for Defense District (PADD)-month level, and the author controls for such
things as stocks and hurricane activity. Similarly to Kendix and Walls (2010), Chesnes concludes that refinery outages “ca n have an economically significant effect on product prices” (p. 333). He finds that product prices are positively related to outages.
Fink et al. (2010) study how tropical storms influence petroleum product prices. The authors examine the effect of tropical storm forecasts on the 3-2-1 crack spread—the difference between refined petroleum and crude oil prices focusing on refinery activity in the Gulf Coast region. The authors test the effect of the release of the leading seasonal hurricane forecast in the Atlantic basin, the Gray-Klotzbach forecast from Colorado State University, on the crack spread market and find important economic effects. Prices appear to reflect storm effects at the 24-h forecast horizon. Further, the magnitude is significant—category 4 hurricanes in this region increase refined petroleum prices relative to crude oil by about 13.5%.
In a follow-up study, Fink and Fink (2014) also find that crack spread prices are affected by seasonal hurricane forecasts. Their approach is to test the effect of the release of the Gray-Klotzbach forecasts in the Atlantic basin (which includes the Gulf of Mexico) on the crack spread market. The authors show that 3-2-1 crack spread prices increase by more than 9% when the June forecast for the upcoming season increases by one standard deviation.
Blair and Rezek (2008) find deviations from historic price
pass-through patterns during the immediate post-Hurricane Katrina period. Although gasoline price pass-through patterns have largely returned to their long-term equilibrium, evidence indicates that asymmetry, previously not evident, may now exist.
4. Evidence on international market integration
Studying the question of whether petroleum product prices have become more integrated over time, Zavaleta et al. (2015) point out that, due to changes in the petroleum refining industry, trade in refined petroleum products has become more widespread and global. The authors investigate the convergence of prices for refined products across different international markets. They examine data on refined products from market centers in the United States and Europe and test for convergence of prices using standard time-series techniques. The researchers base their analysis on tests for cointegration between pairs of prices, such as New York Harbor gasoline and Rotterdam-Amsterdam gasoline. They do not examine relations between Brent prices and New York Harbor gasoline prices. The data plainly show that markets for refined petroleum products have become more interrelated as Europe has begun exporting its gasoline surplus and global demand for petroleum products approaches refining capacity. Their results suggest that the U.S. and European markets for oil and refined products are integrated.
5. Reduced form models of petroleum product prices
5.1. Overview
An alternative to modeling spot prices explicitly from fundamentals is to model the price process using a reduced form structure. Models in this class do not directly model fundamental supply and demand changes but are structured to capture the net effects of these factors without explicitly modeling the underlying forces. These models typically propose one or more sources of underlying uncertainty that contribute to the time series variation in price changes. Examples include general random behavior, mean reversion in the price, price jump activity, random behavior of the convenience yield, stochastic interest rate behavior, and time-varying volatility.
5.2. Model specifications
The genesis of these models can be traced back at least to Black (1976a) in his work on commodity derivatives. Modern thought on the construction of these types of models for oil prices stems from the work of Brennan and Schwartz (1985) and later Schwartz (1997). Numerous extensions of the basic framework by Schwartz and his coauthors as well as others have appeared in the literature.
Reduced form models are designed around a framework in which one or more sources of randomness (commonly referred to as random factors) contribute to the total randomness of spot price change. A single-factor framework is essentially a model with a single source of
“net” uncertainty. The fundamental source of the randomness, however, is not explicitly modeled, but it is implied that the factor reflects the net effect of all fundamental sources of uncertainty. Numerous authors have studied the issue of whether excessive speculative trading activity can disrupt or increase the level of prices and the volatility (randomness) of prices. Non-structural models are agnostic on this potential force. If such effects do exist, these models capture those influences along with all other fundamental randomness in a reduced-form manner. This is not to say that one could not construct a structural model built up from fundamentals involving stochastic supply and demand conditions (Seppi, 2002). Such an approach, however, requires assumptions about the functional forms of the demand and supply functions. The benefits of such models are that they permit key parameters to be functionally related to “non-price” data, such as the weather.
The notion of the “convenience yield” plays an important role in many non-structural models and is a fundamental element of the modern theory of storage, so a brief comment is warranted. Brennan and Schwartz (1985) define the convenience yield as follows: “The convenience yield is the flow of services that accrues to an owner of the physical commodity but not to the owner of a contract for future delivery of the commodity” (p. 139). They point out that “competition among po tential storers will ensure that the net convenience yield of the marginal unit of inventory
will be the same across all individuals who hold positive inventories” (p. 139). Most reduced form models that incorporate a convenience yield actually use the net convenience yield measured as the convenience yield minus the cost of carry, where the cost of carry includes storage costs as well as borrowing costs.
中文
石油产品定价特征：调查
路易斯·埃德灵顿，奇特鲁·费尔南多等
摘要
我们回顾并综合了与石油产品定价有关的几个因素的经验证据：（1）石油产品价格的一般分布特征；（2）炼油厂停运，极端天气和类似情况对产品价格的影响；（3）石油产品价格发现的方式；（4）石油产品期货价格对未来现货价格的预测准确性；（5）投机对产品价格的影响。

关键字：石油产品定价，商品，能源经济学
1.引言
本文是由两部分组成的系列文章的第二篇，该系列文章概述了有关石油产品期货价格的行为和决定因素的现有文献。

Ederington等（2018a）着眼于石油产品价格与石油价格之间的关系。

在这一部分中，我们转向石油产品价格的总体分布特征，炼油厂停运和天气等基本因素对产品价格的影响，石油产品价格发现的方式以及石油产品期
货的预测准确性价格为未来现货价格。

近年来，致力于基本供需因素或过度投机是否影响了原油价格走势的问题的研究激增。

但是，很少有人关注投机活动是否会影响石油产品价格。

我们还将回顾有关该主题的研究。

2.价格和库存
储存理论（Kaldor，1939；Working，1949）预测价格波动和价格水平与库存水平成反比。

当库存很少或没有库存作为缓冲时，供需失衡可能导致价格急剧变化。

此外，价格和波动率将呈正相关，因为两者都与库存呈负相关。

Smith（2009）等人提出的另一种论点强调，由于潜在的需求曲线和供给曲线具有价格弹性，因此油价的波动性可能很高，以至于对供给或需求的冲击都会立即反映在价格中。

最后，波动率和库存之间的关系也可以相反。

也就是说，波动性可能会影响库存水平。

例如，正如Pindyck（2004）所建议的那样，高油价波动增加了现在生产的机会成本，而后期生产则相反。

也就是说，等待观察将来的现货价格是否大于当前的现货价格。

权衡这些观点后，史密斯（Smith（2009））指出价格波动为持有库存提供了动力，但由于库存成本高昂，它们可能不足以完全抵消需求和供应的刚性。

戈顿等（2012）研究石油产品期货的超额收益（他们称为风险溢价）与库存之间的关系。

他们发现，风险溢价的时间序列变化和横截面变化与取暖油，汽油和原油的库存水平成反比。

但是，尽管记录了所有三种商品的负相关关系，但从统计上看，在常规水平上，没有一个与零之间存在差异。

作者将弱的统计显着性水平归因于库存计量误
差的事实。

美国能源信息署（U.S. Energy Information Administration，2014）最近完成的一项研究着重于基准价格（布伦特和西德克萨斯中质油）价格变化与汽油价格之间的关系。

该研究还提出了有关汽油库存与先前五年平均值的偏差变化对汽油价格的条件影响的结果。

作者通常发现库存偏差与汽油价格变化之间存在反比和统计上的显着关系。

Pindyck（2004）建立了一个库存，现货价格和期货价格的结构模型，该模型明确考虑了波动的作用，并使用1984-2001年期间原油，取暖油和汽油的每日和每周数据估算了该模型。

每周波动率估计为调整后每日对数价格变动的样本标准偏差。

Pindyck（2004）发现现货价格，库存和便利收益不会引起原油的波动，因此得出结论，波动是一个外生变量。

但是，他还发现该模型在原油市场上的表现不佳。

对于取暖用油，挥发度的变化会影响便利产量，并在较小程度上影响库存，但影响不大。

在原油和汽油市场上，没有强有力的证据表明这种影响。

此外，尽管取暖油波动的变化可以帮助解释现货期货价差（便利收益率）的变化，但不能解释现货价格本身。

Pindyck（2004）得出结论，该结果符合取暖油的理论预测，而不适合原油或汽油。

Pindyck推测，混合结果可能是模型规格不正确的产物，或者市场变量对生产决策的影响要比每周数据所捕获的慢。

Pindyck还指出，投机可能会影响价格波动，而模型中并未考虑这一因素。

Kaufmann等（2009年）研究了石油价格，汽油价格，石油和汽油的库存水平，炼油厂利用率以及替代燃料（天然气）价格之间的关
系。

他们对石油价格变化如何通过能源供应链水平和/或垂直传递感兴趣。

作者将“水平变速箱定义为在相似处理阶段由燃料之间的链接所产生的变化，而垂直变速箱则是由石油供应链中的上游/下游链接所产生的变化”（第644页）。

他们使用每周和每季度的观测值来估计系列中的矢量误差校正模型，并从因果关系检验中得出结论，原油价格是整个石油供应链行为的重要决定因素。

通过分析脉冲响应函数，他们发现对原油价格的冲击会影响库存行为，炼油厂利用率和车用汽油价格，并横向传播到天然气市场。

Kilian（2010）以及Bilgin和Ellwanger（2017）的研究都强调了库存（石油库存和石油产品库存）在汽油需求和价格形成中的作用。

Kilian（2010）研究了全球原油市场与美国燃料消耗之间的关系。

他发现与美国汽油消费量变化有关的重大油价影响。

Bilgin和Ellwanger（2017）开发并估算了石油和汽油市场的模型，该模型反映了一个基本前提，即“原油需求来自转化为石油产品的需求”。

该模型包括石油和石油产品库存。

作者发现，相对于原油价格，全球燃料需求缺乏弹性。

3.炼油厂停运，与天气有关的因素和产品价格
Kendix and Walls（2010）着眼于2002年1月至2008年9月美国每周批发汽油价格，量化了炼油厂停运对石油产品价格的影响。

作者将精炼装置的产量与特定的批发汽油市场进行匹配，然后估算面板数据的回归并量化精炼装置中断对批发汽油价格的影响。

它们控制特定时间的影响，特定城市的影响，特定燃料的影响，炼油厂浓度以及其
他可能影响精炼石油产品价格的因素。

他们的结果表明，炼油厂停运对炼油产品价格具有统计上显着的积极影响，并且对于某些燃料混合物，这种影响的程度更大。

Chesnes（2015）还研究了当前石油产品价格的炼油厂停工与未来炼油厂投资之间的关系。

作者研究了2001年至2011年期间计划内和计划外炼油厂停机的详细炼油厂级数据。

数据按每月频率测量，其中包括研究的批发价格。

停机总计达到国防部石油管理局（PADD）月的水平，作者控制库存和飓风活动。

与肯迪克斯和沃尔斯（Kendix and Walls，2010）相似，Chesnes得出结论，炼油厂停运“可能对产品价格产生经济上的重大影响”（第333页）。

他发现产品价格与停工成正比。

Fink等（2010）研究热带风暴如何影响石油产品价格。

作者研究了热带风暴预报对3-2-1裂纹扩展的影响，后者是成品油和原油价格之间的差异，重点在于墨西哥湾沿岸地区的炼油活动。

作者测试了大西洋盆地领先的季节性飓风预报（科罗拉多州立大学的Gray-Klotzbach预报）的释放对裂缝传播市场的影响，并发现了重要的经济影响。

价格似乎反映了24小时预报范围内的风暴影响。

此外，幅度很大，该地区的4类飓风使成品油价格相对于原油上涨了13.5％。

在后续研究中，芬克和芬克（Fink and Fink，2014年）还发现，裂解价差价格受季节性飓风预报的影响。

他们的方法是检验在大西洋沿岸盆地（包括墨西哥湾）发布的Gray-Klotzbach预测对裂解市场的影响。

作者表明，当即将到来的季节的6月预测增加一个标准差时，
3-2-1裂纹点差价格将增长9％以上。

布莱尔和雷泽克（Blair and Rezek，2008年）发现，在卡特里娜飓风后即期，与历史价格传递模式存在偏差。

尽管汽油价格的传递模式已基本恢复到长期均衡状态，但有证据表明，以前可能不明显的不对称现在可能存在。

4.国际市场一体化的证据
Zavaleta等人（2015）研究了石油产品价格是否随着时间的推移变得更加一体化的问题。

指出，由于石油精炼行业的变化，成品油产品的贸易已变得更加广泛和全球。

作者研究了不同国际市场上成品油价格的趋同。

他们检查了来自美国和欧洲市场中心的成品的数据，并使用标准时间序列技术测试价格的趋同性。

研究人员的分析基于对价格对之间的协整检验，例如纽约港汽油和鹿特丹-阿姆斯特丹汽油。

他们没有研究布伦特原油价格与纽约港汽油价格之间的关系。

数据清楚地表明，随着欧洲开始出口其过剩的汽油以及全球对石油产品的需求接近炼油能力，成品油产品的市场之间的联系更加紧密。

他们的结果表明，美国和欧洲的石油和精炼产品市场已经整合。

5.石油产品价格的简化模型
5.1总览
从基本面出发显式建模现货价格的一种替代方法是使用简化形式的结构对价格过程进行建模。

此类中的模型不直接建模基本的供求变化，而是构造为捕获这些因素的净效应，而无需明确建模潜在的力量。

这些模型通常提出一个或多个潜在的不确定性来源，这些因素会
导致价格变化的时间序列变化。

例子包括一般的随机行为，价格的均值回归，价格跳动，便利收益的随机行为，随机利率行为和时变波动。

5.2模型
这些模型的起源至少可以追溯到布莱克（Blacka，1976a）关于商品衍生工具的工作。

布伦南和史瓦兹（Brennan and Schwartz，1985）以及后来的史瓦兹（Schwartz，1997）的工作，都对构建这类油价模型进行了现代思考。

Schwartz和他的合著者以及其他作者对基本框架进行了无数扩展。

简化形式的模型是围绕一个框架设计的，在该框架中，一个或多个随机源（通常称为随机因素）对现货价格变化的总体随机性起作用。

单因素框架本质上是具有“净”不确定性单一来源的模型。

然而，随机性的基本来源并未明确建模，但暗示该因素反映了所有不确定性基本来源的净效应。

许多作者研究了过度的投机性交易活动是否会破坏或增加价格水平以及价格的波动性（随机性）的问题。

非结构模型无法确定这种潜在的作用力。

如果确实存在此类影响，则这些模型将以简化形式捕获这些影响以及所有其他基本随机性。

这并不是说人们无法构建一个由涉及随机供需条件的基本原理构建的结构模型（Seppi，2002）。

但是，这种方法需要对需求和供应功能的功能形式进行假设。

这种模型的好处在于，它们允许关键参数在功能上与“非价格”数据（例如天气）相关。

“便利收益”的概念在许多非结构模型中起着重要的作用，并且是现代存储理论的基本要素，因此有必要作一简要评论。

Brennan
和Schwartz（1985）对便利收益的定义如下：“便利收益是指实物商品的所有者而不是未来商品交付合同的所有者的服务流”。

他们指出，“潜在存储者之间的竞争将确保所有拥有正库存的个人的库存边际单位的净便利收益将是相同的”（第139页）。

大多数合并了便利收益的简化表格模型实际上都使用净便利收益来衡量，即便利收益减去携带成本，其中携带成本包括存储成本和借贷成本。

文献出处：Journal of Commodity Markets，V olume 14, June 2019, Pages 1-15。