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Forecasting the Price of Oil

01 May 2011-Research Papers in Economics (C.E.P.R. Discussion Papers)-Vol. 2, pp 427-507
TL;DR: In this article, the authors address some of the key questions that arise in forecasting the price of crude oil and evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions.
Abstract: We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real or nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures markets in forecasting the price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?
Citations
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Journal ArticleDOI
TL;DR: This article showed that both the short-run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s, which implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run-up and subsequent collapse in the price of oil.
Abstract: There has been a systematic increase in the volatility of the real price of crude oil since 1986, followed by a decline in the volatility of oil production since the early 1990s. We explore reasons for this evolution. We show that a likely explanation of this empirical fact is that both the short-run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s. This implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run-up and subsequent collapse in the price of oil. Our analysis suggests that the variability of oil demand and supply shocks actually has decreased in the more recent past preventing even larger oil price ‡uctuations than observed in the data. JEL classi…cation: E31, E32, Q43

385 citations

Book ChapterDOI
TL;DR: In this article, the authors address some of the key questions that arise in forecasting the price of crude oil and evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future oil demand and oil supply conditions.
Abstract: We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real and nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures prices in forecasting the spot price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future oil demand and oil supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?

329 citations

Journal ArticleDOI
TL;DR: This paper investigated how the dynamic eects of oil supply shocks on the US economy have changed over time and found that a typical oil supply shock is currently characterized by a much smaller impact on world oil production and a greater eect on the real price of crude oil, but has a similar impact on US output and in-ability as in the 1970s.
Abstract: We investigate how the dynamic eects of oil supply shocks on the US economy have changed over time. We …rst document a remarkable structural change in the oil market itself, i.e. a considerably steeper, hence, less elastic oil demand curve since the mid-eighties. Accordingly, a typical oil supply shock is currently characterized by a much smaller impact on world oil production and a greater eect on the real price of crude oil, but has a similar impact on US output and in‡ation as in the 1970s. Second, we …nd a smaller role for oil supply shocks in accounting for real oil price variability over time, implying that current oil price ‡uctuations are more demand driven. Finally, while unfavorable oil supply disturbances explain little of the "Great In‡ation", they seem to have contributed to the 1974/75, early 1980s and 1990s recessions but also dampened the economic boom at the end of the millennium.

329 citations

Journal ArticleDOI
TL;DR: The authors found that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003, and there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.
Abstract: A popular view is that the surge in the price of oil during 2003-08 cannot be explained by economic fundamentals, but was caused by the increased financialization of oil futures markets, which in turn allowed speculation to become a major determinant of the spot price of oil. This interpretation has been driving policy efforts to regulate oil futures markets. This survey reviews the evidence supporting this view. We identify six strands in the literature corresponding to different empirical methodologies and discuss to what extent each approach sheds light on the role of speculation. We find that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003. Instead, there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.(This abstract was borrowed from another version of this item.)

303 citations

Journal ArticleDOI
TL;DR: This paper showed that recursive vector autoregressive (VAR) models tend to have lower mean squared prediction error (MSPE) at short horizons than forecasts based on oil futures prices.
Abstract: We construct a monthly real-time dataset consisting of vintages for 1991.1–2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25% 1 month ahead and 24% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models...

296 citations

References
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TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
Abstract: Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

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Abstract: In this paper we study both market risks and nonmarket risks, without complete markets assumption, and discuss methods of measurement of these risks. We present and justify a set of four desirable properties for measures of risk, and call the measures satisfying these properties “coherent.” We examine the measures of risk provided and the related actions required by SPAN, by the SEC=NASD rules, and by quantile-based methods. We demonstrate the universality of scenario-based methods for providing coherent measures. We offer suggestions concerning the SEC method. We also suggest a method to repair the failure of subadditivity of quantile-based methods.

8,651 citations

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