Improving the Forecasting Accuracy of Crude Oil Prices

Yin, Xuluo; Jian-gang, Peng; Tang, Tian

doi:10.3390/su10020454

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…Arfaoui (2018) drew the conclusion that the short-run and long-run elasticities exist between spot and futures prices and between crude and refined oil prices, with the exception of gasoline, by using an ARDL bounds testing approach [15]. Yin et al (2018) made an oil price forecast by using many predictor variables with a new time-varying weight combination approach to improve the accuracy. Good prediction results can be provided by using the above model based on the near linear relationships [4].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

2021

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Examining the price relationships of Brent Crude with 78 global commodities, our study shows that the spot price of a certain commodity, New York Harbor No. 2 Heating Oil Spot Price FOB, can serve as an auxiliary forecasting index of the rise and fall of the monthly Brent Crude oil price. With an innovative view for evaluating the price relationship and prediction based on simple, practical measurement, our findings provide a helpful auxiliary index tool for investors and analysts by offering a high success rate (82.98%) and predicting the rise and fall of the monthly Brent Crude oil price three weeks in advance.

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Section: Literature Reviewmentioning

confidence: 99%

“…Many methods have been developed to predict crude oil price, and some of them have reached accuracies of around 61% [4]. A lot of room for improvement still exists for effective forecasting of crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

2021

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“…Introducing time lags into the analysis suffers from similar shortcomings. The time-varying weight-combination approach appears to alleviate some of the methodological challenges inherent in the statistical analysis of crude oil prices time series [37].…”

Section: Methodsmentioning

confidence: 99%

Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest

2020

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The paper postulates that enhanced informational efficiency and signal processing capacity, which have characterized the evolution of commodity markets’ architecture during the last two decades, have rendered commodity prices more robust with respect to external shocks. Our econometric analysis of times series over 2001–2015 revealed a persistent decline in the responsiveness of crude oil prices to inflows of information concerning potentially supply-disruptive events. International news on terrorist attacks involving damage to oil infrastructure including those occurring in proximity to oil extraction sites, political unrest, and conflicts of rivaling factions are all documented to exercise a decreasing impact on oil price volatility both over short and medium observation spans. The previously observed spikes in oil prices accompanying similar disruptive events in OPEC countries are also shown to flatten over time as price sensitivity to information shocks declines. The discovered weakening of market response becomes more pronounced from the mid-2000s, which corresponds to the period of rapid algorithmization of commodity trading.

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“…If av = "dma", then the original DMA averaging scheme is performed. If av = "mse" then predictive densities in Equation 5are replaced by the inverses of Mean Squared Errors of the models [175,176]. If av = "hr1", then they are replaced by Hit Ratios (assuming time-series are in levels).…”

Section: Fundamental Functionsmentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.

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Improving the Forecasting Accuracy of Crude Oil Prices

Cited by 12 publications

References 27 publications

An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

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