Short-term predictability of crude oil markets: A detrended fluctuation analysis approach

Alvarez-Ramı́rez, José; Álvarez, Jesús; Rodríguez, Eduardo

doi:10.1016/j.eneco.2008.05.006

Cited by 208 publications

(82 citation statements)

References 33 publications

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“…The results are broadly in line with the findings of Elder and Serletis (2008), and Alvarez-Ramirez et al (2008) in that we show evidence of time-varying autocorrelations for weekly oil-return dynamics over the study period, except for some relatively short sub-periods. However, the hypothesis of convergence towards efficient behavior over time cannot be confirmed, since the intensity of oil-return predictability has tended to increase rather than decrease in recent years.…”

Section: Resultssupporting

confidence: 80%

“…They estimate the Lo (1991)'s modified Hurst exponent by rescaled range analysis which corrects for short-term autocorrelations, and find that crude-oil markets have become more efficient over time. By implementing a de-trended fluctuation analysis to reduce the effects of nonstationarities and trends in the estimation of the Hurst exponents, Alvarez-Ramirez et al (2008) confirm the findings of Tabak and Cajueiro (2007) in that crude-oil markets converge towards weak-form efficiency over the long term. Furthermore, their results are consistent with the existence of the timevarying short-term predictability reported in Elder and Serletis (2008).…”

Section: Introductionsupporting

confidence: 56%

“…and Alvarez-Ramirez et al (2008), who cannot reject the presence of meaningful autocorrelations in the Brent (North Sea -Europe), WTI (USA), and Dubai (UAE) crude-oil price returns for time horizons shorter than one month by estimating the DFA-augmented (Detrended Fluctuation Analysis) Hurst exponent.…”

Section: Time-varying Short-term Predictability In Oil Marketsmentioning

confidence: 99%

“…It allows us to study the convergence of crude oil prices towards efficient prices over time and to examine the speed of this convergence process while controlling for the presence of conditional heteroscedasticity and leptokurtic distribution of crude-oil returns. Compared to the related literature, our contribution is primarily related to the work of Tabak and Cajueiro (2007) and Alvarez-Ramirez et al (2008) in the sense that we are also interested in the dynamics rather than the average behavior of crude-oil prices, but our methodology is different. This then allows us to compare the results obtained across studies.…”

Section: Introductionmentioning

confidence: 99%

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Time-varying predictability in crude-oil markets: the case of GCC countries

2010

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This paper uses a time-varying parameter model with generalized autoregressive conditional heteroscedasticity effects to examine the dynamic behavior of crude-oil prices for the period 1997-2008. Using data from four countries of the Gulf Cooperation Council, we find evidence of short-term predictability in oil-price changes over time, except for several short sub-periods. However, the hypothesis of convergence towards weak-form informational efficiency is rejected for all markets. In addition, we explore the possibility of structural breaks in the time-paths of the estimated predictability indices and detect only one breakpoint, for the oil markets in Qatar and United Arab Emirates. Our empirical results therefore call for new empirical research to further gauge the predictability characteristics and the determinants of oil-price changes.

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Section: Resultssupporting

confidence: 80%

Section: Introductionsupporting

confidence: 56%

Section: Time-varying Short-term Predictability In Oil Marketsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-varying predictability in crude-oil markets: the case of GCC countries

2010

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“…Barone et al (1998) suggested a semi-parametric modeling technique for oil price forecasting. Further, Alvarez et al (2008) showed in their research that the random walk-type behavior in energy futures prices, thus the autocorrelation in oil prices diminishes over time. Adrangi et al (2001) tested the presence of low-dimensional chaotic structure in crude oil, heating oil, and unleaded gasoline futures prices with their sample starting by the early 1980s.…”

Section: Introductionmentioning

confidence: 99%

Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

Bildirici

Ersin

2015

Pet. Sci.

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In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.

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