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

Arouri, Mohamed El Hédi; Dinh, Thanh Huong; Nguyen, Duc Khuong

doi:10.1016/j.enpol.2010.03.065

Cited by 46 publications

(13 citation statements)

References 39 publications

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“…The choice of this model can be justified empirically by analysis of the autocorrelation functions of series and squared series, which show an hyperbolic decrease to zero as lags increase. In addition, the associated spectral densities seem not to be bounded, which may indicate the presence of long-memory behavior in both mean and variance 9 .…”

Section: Marginal Distributionsmentioning

confidence: 97%

“…It should be stressed that, within the FIAPARCH model, we can test for the restrictions embodied in the FIGARCH model, i.e., 0 ς = and 2 δ = relying on the likelihood ratio (LR) type test. Formally, the LR test is a 9 We do not report autocorrelation functions and spectral densities. These are available on request.…”

Section: Marginal Distributionsmentioning

confidence: 99%

“…An important precondition for the validation of the linear models is the stability of the models and the invariability of the parameters over time. In practice, this assumption is far from being satisfied due to the presence of structural breaks [9] and regime change [10]. Consequently, the parameters are time-varying and the model seems to be non-linear.…”

Section: Introductionmentioning

confidence: 99%

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Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

Boubaker¹,

Sghaier²

2016

OJS

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This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing pre-crisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre-and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching timevarying copula model performs better than the time-varying copula model.

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Section: Marginal Distributionsmentioning

confidence: 97%

Section: Marginal Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

Boubaker¹,

Sghaier²

2016

OJS

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“…It should be noticed that models pre-named "Bayesian" are the special cases of their more general encompassing combination schemes, just that their forgetting parameters are α = 1 = λ. This name-based emphasis is done to stress the particular forgetting factor values, corresponding to equal-weighting in Equation (1) and no variability of regression coefficients assumed.…”

Section: Median Probability Model (Med)mentioning

confidence: 99%

Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

Drachal

2018

Sustainability

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Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters' (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold-Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second "best" model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

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“…For example, [7,25] show important differences among developed and emerging markets. Recent literature [6,8,9,11] shows that the degree of memory in a given mar-ket or stock is contingent to some of their characteristics.…”

Section: Introductionmentioning

confidence: 99%

Analysis of financial indices by means of the windowed Fourier transform

Machado

Duarte

2012

SIViP

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The goal of this study is to analyze the dynamical properties of financial data series from nineteen worldwide stock market indices (SMI) during the period 1995-2009. SMI reveal a complex behavior that can be explored since it is available a considerable volume of data. In this paper is applied the window Fourier transform and methods of frac-tional calculus. The results reveal classification patterns typ-ical of fractional order systems.

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

Cited by 46 publications

References 39 publications

Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

Analysis of financial indices by means of the windowed Fourier transform

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