The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality

By Stelios D. Bekiros, Cees G. H. Diks. 2008

377 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/488bc26a20da5b6cb4f68fe9dcc59f51ab216596

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In Economics and Econometrics

Citations in the corpus

Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests (2016, background): “…Jones (1994) and subsequent research papers have provided further evidence in a nonlinear setting with respect to various financial variables (e.g., Silvapulle and Choi, 1999; Chen and Wuh-Lin, 2004; Diks and Panchenko, 2006; Bekiros and Diks, 2008a, 2008b; Shin et al., 2013; and Bekiros, 2014).”
The crude oil market and the gold market: Evidence for cointegration, causality and price discovery (2010, methodology): “For this topic, it has been universally acknowledged that one of the main methods should be Granger causality test approach, especially the linear Granger causality test (Yang, 2000; Jiao et al., 2004, 2005; Tang, 2008; Erdal et al., 2008; Bekiros and Diks, 2008; Henriques and Sadorsky, 2008; Zhang ”
Market efficiency of oil spot and futures: A mean-variance and stochastic dominance approach (2010, neutral): “Bekiros and Diks (2008) test the existence of linear and nonlinear causal lead–lag relationships between spot and futures prices of WTI crude oil.”
World oil and agricultural commodity prices: Evidence from nonlinear causality (2011, methodology): “Following Bekiros and Diks (2008), the nonlinear Granger causality analysis is carried out in two steps.”
The economic value of co-movement between oil price and exchange rate using copula-based GARCH models (2011, methodology): “Previous research, such as Bekiros and Diks (2008), and Chang et al. (forthcoming), has commonly used multivariate GARCH models as a means of estimating time-varying dependence structures, but this is often based on severe restrictions in order to guarantee a welldefined covariance matrix.”
Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models (2020, background): “We choose WTI to represent the oil market because it is one of the foremost global oil markets and is used predominantly as a benchmark in world oil pricing in existing studies (Bekiros and Diks, 2008).”
Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach (2015, methodology): “, Bekiros and Diks, 2008; Chiou-Wei et al., 2008; Diks and Panchenko, 2006;Hiemstra and Jones, 1994). In this paper, thenonlinear Granger test proposed by Diks and Panchenko (2006), which effectively addresses the over-rejection problem of the algorithm of Hiemstra and Jones (1994) by introducing a ”