Open AccessPosted Content
Forecasting the Price of Oil
TLDR
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?read more
Citations
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Can investor attention predict oil prices
Liyan Han,Qiuna Lv,Libo Yin +2 more
TL;DR: In this paper, the predictive power of investor attention on oil prices was investigated by using the Google search volume index (SVI) based on a broad set of words related to oil-related variables and terms that are directly linked to real economy to measure investor attention.
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Real-Time Analysis of Oil Price Risks Using Forecast Scenarios
TL;DR: In this article, the authors show how policy-relevant forecast scenarios can be constructed from recently proposed structural vector autoregressive (VAR) models of the global oil market and how changes in the probability weights attached to these scenarios affect the upside and downside risks embodied in the baseline real-time oil price forecast.
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Does the volatility of commodity prices reflect macroeconomic uncertainty
TL;DR: In this paper, a structural threshold vector autoregressive (TVAR) model was used to estimate the impact of macroeconomic uncertainty on commodity prices and their volatility, and the authors found that both agricultural and industrial commodity markets are highly sensitive to the variability and the level of uncertainty, while the impact on precious metals is more parsimonious.
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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.
ReportDOI
Can Oil Prices Forecast Exchange Rates
TL;DR: In this article, the authors investigated whether oil prices have a reliable and stable out-of-sample relationship with the Canadian/U.S dollar nominal exchange rate and showed that there is a very short-term relationship at the daily frequency, which is rather robust and holds no matter whether they use contemporaneous (realized) or lagged oil prices in their regression.
References
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Time series analysis
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.
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Coherent Measures of Risk
TL;DR: In this paper, the authors present and justify a set of four desirable properties for measures of risk, and call the measures satisfying these properties "coherent", and demonstrate the universality of scenario-based methods for providing coherent measures.
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Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
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Impulse response analysis in nonlinear multivariate models
TL;DR: In this paper, the authors present a unified approach to impulse response analysis which can be used for both linear and nonlinear multivariate models and demonstrate the use of these measures for a nonlinear bivariate model of US output and the unemployment rate.
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The Economics of Exhaustible Resources
TL;DR: In this article, a discussion is confined in scope to absolutely irreplaceable assets, including peculiar problems of mineral wealth, free competition, maximum social value and state regulation, monopoly, value of a mine monopoly, retardation of production under monopoly, price effects from cumulated production, and the author's mathematically derived optimum solutions.