Forecasting short-run crude oil price using high- and low-inventory variables

Ye, Michael; Zyren, John; Shore, Joanne

doi:10.1016/j.enpol.2005.03.017

Cited by 106 publications

(67 citation statements)

References 17 publications

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“…En otro trabajo, Arouri y Nguyen (2010) utilizan técnicas econométricas para analizar la relación de los cambios en los precios del petróleo y del gas con el comportamiento de diversas actividades económicas en Europa, encontrando errores en los pronósticos con RMSE entre 1.84 y 1.95 y MAPE entre 0.9817 y 1.2173. También se ha estudiado el efecto del nivel de inventarios de crudo en los países miembros de la OCDE en el pronóstico de sus precios mensuales (Ye et al, 2006). Para tal efecto realizan análisis estadístico de las series de tiempo de inventarios y precios obteniendo resultados satisfactorios con errores RMSE entre 1.7441 y 2.9266.…”

Section: Introductionunclassified

Pronóstico del Precio del Petróleo mediante Redes Neuronales Artificiales

2014

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ResumenEste trabajo propone un modelo basado en redes neuronales artificiales para el pronóstico del precio internacional del petróleo. Para el desarrollo del modelo se usan datos de precios de la literatura para el petróleo de referencia WTI (West Texas Intermediate) transado principalmente en la Bolsa Mercantil de Nueva York. Se utilizan cuatro estructuras de redes incluyendo como entradas la serie de precios diarios en la primera, la serie de precios más el índice del dólar estadounidense DXY en la segunda, la serie de precios más el índice S&P500 en la tercera y la serie de precios más los índices DXY y S&P500 en la cuarta. Se prueban diferentes configuraciones de redes neuronales utilizando una serie de seis meses, donde los datos de los primeros cinco se utilizan para entrenamiento dejando el último mes para verificar las capacidades predictivas del modelo. Se analiza también el efecto de incluir el grado de aversión al riesgo de los inversionistas por medio de los índices DXY y S&P500 como variables adicionales de entrada. Los resultados muestran un buen comportamiento de las redes neuronales con bajos errores en aprendizaje y en predicción Palabras clave: mercado del petróleo, redes neuronales artificiales, pronóstico de precios Oil Price Forecast using Artificial Neural Networks AbstractAn artificial neural network model to forecast oil international price is proposed in this work. To develop the model, price data taken from the literature for the WTI reference oil (West Texas Intermediate) traded mainly in New York Mercantile Exchange are used. Four network structures, including the daily price series in the first one, the price series plus the dollar index DXY in the second one, the price series plus the S&P500 index in the third one and the price series plus the DXY and S&P500 indexes in the fourth one are used. Different neural networks configurations are analyzed using a series of six months, where data for five months are used for training patterns and the next month is left for testing the predictive capabilities of the model. The effect of including investors risk aversion using the DXY and S&P500 indexes as alternative input patterns is also analyzed. The results show good performance of the neural networks both during learning and prediction.

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Section: Introductionunclassified

Pronóstico del Precio del Petróleo mediante Redes Neuronales Artificiales

2014

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“…In the short term, inventory levels have an effect on crude oil prices, see Pindyck (2004). The use of inventory data has been used for short term (up to three months ahead) forecasting by Ye, Zyren and Shore (2006). In the following two subsections, inventory data is not considered, this is because it is of lower frequency than the price series modelled.…”

Section: Review Of Pricing Modelsmentioning

confidence: 99%

Oil prices — Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

Meade

2010

Energy Economics

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Abstract. For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation process with GARCH and no mean reversion. Acknowledgement:The author would like to express his gratitude to three anonymous referees for their insightful comments on an earlier version of the paper which led to an improvement in its content and exposition.

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“…On the other hand, the increase in the interest rate reduces the motivation for maintaining the oil stock (oil on ship). Oil market studies show that one of the influencing factors in determining the oil spot prices is the change in maintained crude oil stock on the ship (Ye, 2006).…”

Section: A -By Supplymentioning

confidence: 99%

Investigating the Behavior of Iran's Light Crude Oil Price in Short Term

Razavi¹,

Salimifar²,

Mostafavi³

et al. 2016

MAS

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Investigate the causes of changing the oil price and modeling for predicting its volatility has always been one of the most important fields of Iran's economic literature study due to its position in Iran's economy. On the other hand, oil price volatility lead to the difficulty in the development programs. Empirical studies show that oil prices volatility are caused the structural bottlenecks (trade balance bottleneck, budget bottlenecks, etc.) in Iran's economic.

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Forecasting short-run crude oil price using high- and low-inventory variables

Cited by 106 publications

References 17 publications

Pronóstico del Precio del Petróleo mediante Redes Neuronales Artificiales

Pronóstico del Precio del Petróleo mediante Redes Neuronales Artificiales

Oil prices — Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

Investigating the Behavior of Iran's Light Crude Oil Price in Short Term

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