scispace - formally typeset
Journal ArticleDOI

Uncertainty, Expectations, and Fundamentals: Whatever Happened to Long-Term Oil Prices?

Bassam Fattouh, +1 more
- 01 Mar 2011 - 
- Vol. 27, Iss: 1, pp 186-206
TLDR
In this paper, the authors suggest an interpretation of the long-term behavior of oil prices based on the insights of two models, namely signal extraction and Bayesian updating, and show that if the variability of the spot price increases and/or if the spot prices remains higher over a sustained period of time than anticipated by investors, then the probability distribution of the parameter capturing the speed of mean reversion will shift and the expected future price will move closer to the current spot price.
Abstract
One of the major features of the oil market during the 1990s was the relative stability of the long-term oil price. While the spot price exhibited sharp price volatility, that volatility was only partially transmitted to the back end of the futures curve which was anchored around the $20--22 per barrel range. However, as oil prices rose sharply during the boom years, the consensus on the oil price that would balance the long-term fundamentals of the oil market broke down and the whole futures curve became subject to a series of shifts. Our empirical evidence suggests that in the late 1990s and early 2000s there was limited evidence of adjustment between short-term and long-term oil prices. These dynamics, however, changed in early 2005 with the long-term price making most of the adjustment towards the prompt price. We suggest an interpretation of the long-term behaviour of oil prices based on the insights of two models. The first is based on a signal extraction mechanism and shows that when the private beliefs by investors about the long-run determinants of oil prices become less precise relative to the information contained in the current spot price, then the expected future oil price becomes closer to the current spot price. The second model is based on Bayesian updating and shows that if the variability of the spot price increases and/or if the spot price remains higher over a sustained period of time than anticipated by investors, then the probability distribution of the parameter capturing the speed of mean reversion will shift and the expected future price will move closer to the current spot price. Our analysis predicts that in the face of increased uncertainty the long-term and short-term prices are bound to exhibit similar movements. These changes have important consequences on the oil price formation process. Copyright 2011, Oxford University Press.

read more

Citations
More filters
Posted Content

The Role of Speculation in Oil Markets: What Have We Learned so Far?

TL;DR: This article 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.
Journal ArticleDOI

The role of speculation in oil markets: What have we learned so far

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.
Journal ArticleDOI

Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?

TL;DR: In this paper, the analysis of monthly spot oil prices between 1986 and 2015 was performed based on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS) framework.
Journal ArticleDOI

Oil price dynamics, macro-finance interactions and the role of financial speculation.

TL;DR: This paper found that while macroeconomic shocks have been the main real oil price upward driver since mid-1980s, financial shocks have sizably contributed since early 2000s as well, and at a much larger extent since mid 2000s.
References
More filters
Journal ArticleDOI

Statistical analysis of cointegration vectors

TL;DR: In this paper, the authors consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors, and derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimensions.
Book

An Introduction to Multivariate Statistical Analysis

TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
Book

Likelihood-Based Inference in Cointegrated Vector Autoregressive Models

TL;DR: In this paper, a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model is given, with the main emphasis on the derivation of estimators and test statistics through a consistent use of the Guassian likelihood function.
Posted Content

Likelihood-Based Inference in Cointegrated Vector Autoregressive Models

TL;DR: In this paper, the authors give a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model, which has gained popularity because it can capture the short-run dynamic properties as well as the long-run equilibrium behaviour of many non-stationary time series.
Related Papers (5)