Cyclical behavior of crude oil markets and economic recessions in the period 1986–2010

Alvarez-Ramı́rez, José; Martina, Esteban; Ibarra-Valdez, Carlos

doi:10.1016/j.techfore.2011.04.010

Cited by 15 publications

(13 citation statements)

References 44 publications

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“…From these plots, it is not easy to tell the form of , and the one with the smallest AIC is picked. Figure 5 shows a plot of AICs for various models fit for CF ∆ , from which we see that the best model is an ( ) 6,11 ARMA with an AIC of −4684.1. This model has the parameters estimated via the maximum likelihood, summarized in Table 3.…”

Section: Data and Resultsmentioning

confidence: 99%

“…Several ARMA models are fit to the return series and the standardised residuals analysed. For the crude futures return series, the best model turns out to be an ( ) 6,11 ARMA an for the crude spot it is an ( ) this and all the other six series with the residual analysis conforming to the assumption of Gaussian Innovations. GARCH models can therefore adequately model the trends and patterns in the energy markets.…”

Section: Discussionmentioning

confidence: 99%

“…Forecasting of crude oil prices is important for better investment and risk management and policy development, and econometric models are the most commonly used. Various authors have employed time series [3] [4], financial [5] and structural models [6] in forecasting crude oil prices. Financial studies involve asset returns [7]- [9], instead of prices, since, firstly, returns give a complete and scale-free summary of the investment opportunities and secondly, return series have nice statistical properties known as stylised facts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Financial Time Series Modelling of Trends and Patterns in the Energy Markets

Aduda¹,

Weke²,

Ngare³

et al. 2016

JMF

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Precise recognition of a time series path is important to policy makers, statisticians, economists, traders, hedgers and speculators alike. The correct time series path is also a key ingredient in pricing models. This study uses daily futures prices of crude oil and other distillate fuels. This paper considers the statistical properties of energy futures and spot prices and investigates the trends that underlie the price dynamics in order to gain further insights into possible nuances of price discovery and energy market dynamics. The family of ARMA-GARCH models was explored. The trends depict time varying variability and persistence of oil price shocks. The return series conform to a constant mean model with GARCH variance.

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Section: Data and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Financial Time Series Modelling of Trends and Patterns in the Energy Markets

Aduda¹,

Weke²,

Ngare³

et al. 2016

JMF

View full text Add to dashboard Cite

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“…For example, Jammazi and Aloui [12] combine the Haar à trous wavelet model and Markov switching vector autoregressive (MS-VAR) model to analyze three regimes for the influence of the crude oil shocks on the stock market using data from the UK, France, and Japan [12]. Alvarez-Ramirez et al [13] propose an entropy time-asymmetry approach to analyze the dynamics of the crude oil price and macroeconomic data in the USA. They identify the cyclical behavior with the period of 4.5 years [13].…”

Section: Introductionmentioning

confidence: 99%

“…Alvarez-Ramirez et al [13] propose an entropy time-asymmetry approach to analyze the dynamics of the crude oil price and macroeconomic data in the USA. They identify the cyclical behavior with the period of 4.5 years [13]. Martina et al [14] construct a multiscale entropy model to discuss the dynamics and structure of the crude oil prices and the connection between oil price and macroeconomic activity in different time scales.…”

Section: Introductionmentioning

confidence: 99%

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Zha

et al. 2016

Sustainability

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Abstract:Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

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Artificial intelligence methods for oil price forecasting: a review and evaluation

Sehgal

Pandey

2015

Energy Syst

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Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology.

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Cyclical behavior of crude oil markets and economic recessions in the period 1986–2010

Cited by 15 publications

References 44 publications

Financial Time Series Modelling of Trends and Patterns in the Energy Markets

Financial Time Series Modelling of Trends and Patterns in the Energy Markets

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Artificial intelligence methods for oil price forecasting: a review and evaluation

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