Dynamic integration of world oil prices: A reinvestigation of globalisation vs. regionalisation

Ji, Qiang; Fan, Ying

doi:10.1016/j.apenergy.2015.05.117

Cited by 63 publications

(36 citation statements)

References 37 publications

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“…Introduced by Spirtes et al (2000) to quantitatively determine the contemporaneous causal relations among variables, the DAG approach has been widely applied to commodity and finance markets (Awokuse and Bessler 2003;Bessler and Yang 2003;Ji and Fan 2015;Ji and Fan 2016). It is a graph structure that can be determined based on observed correlations and partial correlations.…”

Section: Dag Theorymentioning

confidence: 99%

“…Therefore, unlike the Granger causality approach, the DAG identifies a map that enables the causal order to be uncovered without relying on ad hoc network structures. Widely used in the scientific literature, DAG has only been recently applied to finance (Awokuse and Bessler 2003;Bessler and Yang 2003;Ji and Fan 2015;Ji and Fan 2016). To the best of our knowledge, this is the first application of the DAG method within a large set of financial assets that includes Bitcoin.…”

Section: Introductionmentioning

confidence: 99%

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Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

Bouri

Gupta

et al. 2018

The Quarterly Review of Economics and Finance

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225

104

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We use a data-driven methodology, namely the directed acyclic graph, to uncover the contemporaneous and lagged relations between Bitcoin and other asset classes. The adopted methodology allows us to identify causal networks based on the measurements of observed correlations and partial correlations, without relying on a priori assumptions. Results from the contemporaneous analysis indicate that the Bitcoin market is quite isolated, and no specific asset plays a dominant role in influencing the Bitcoin market. However, we find evidence of lagged relationships between Bitcoin and some assets, especially during the bear market state of Bitcoin. This finding suggests that the integration between the Bitcoin and other financial assets is a continuous process that varies over time. We conduct forecast error variance decompositions and find that the influence of each of the other assets on Bitcoin over a 20-day horizon does not account for more than 11% of all innovations. JEL classifications: G11, G15 market capitalization reached more than 15.6 billion US dollars. Given evidence on the role of Bitcoin as an investment asset, the issue of its causal relationship with other financial assets such as equities, bonds, currencies, and commodities needs to be uncovered. In particular, increased or decreased interdependencies among Bitcoin and other financial assets have potential impacts on global investors' assets allocation decisions and on policymakers in countries that are likely to consider Bitcoin as official digital currencies or part of their foreign reserves. Although numerous studies have so far considered the relationship between Bitcoin and other economic and financial variables, there remains scepticism on the contemporaneous and lagged causal relationship between Bitcoin and numerous financial assets, and a lack of understanding on the integration of the Bitcoin market with the markets of other financial assets. Prior studies consider the relation between Bitcoin and a few financial assets that include: UK equities, EUR/USD, GBP/USD (Dyhrberg 2016), alternative monetary systems (Rogojanu and Badea 2014), metals and currencies (Baur et al. 2015), global macro-financial development (Ciaian et al. 2016b), energy commodities (Bouri et al. 2017c), global uncertainty (Bouri et al. 2017b), and trading volume (Balcilar et al. 2017). While Brière et al. (2015) point toward the low correlation of Bitcoin with traditional assets and commodities, they simply rely on the correlation coefficient and do not account for structural breaks. Interestingly, Bouri et al. (2017a) use a correlation approach based on the dynamic conditional correlation model of Engle (2002) and consider the relation between Bitcoin returns and the returns of several international equity market indices as well as commodities. However, the authors focus only on the hedge and safe haven property of Bitcoin by employing a pairwise dynamic correlation-based model. Similarly, Bouri et al. (2017c) consider the pairwise relation between Bitcoin returns and fl...

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Section: Dag Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

Bouri

Gupta

et al. 2018

The Quarterly Review of Economics and Finance

Self Cite

225

104

View full text Add to dashboard Cite

show abstract

“…Unlike the results from the Nigerian Bonny Light, one can argue that the strong causal effects observed in the table are largely driven by the effect of geopolitical risks on the Nigerian Bonny Light. This is an important finding given the evidence in Ji and Fan (2015) that the influence of Nigerian prices on other crude oil prices rose after 2010 and suggests that the Nigerian oil market could serve as the transmitter of possible geopolitical risk effects on other crude oil markets during volatile periods. In sum, when we account for non-linearity and regime changes, we observe weak evidence in favour of GPRs affecting returns in the oil market, compared with the misspecified linear test results, while the GPR effect on returns is largely concentrated on the Nigerian market.…”

Section: Resultsmentioning

confidence: 68%

“…All of the above‐mentioned studies relating GPRs with movements in the oil markets, however, are restricted to either the West Texas Intermediate (WTI) oil price, or a measure of world oil price, via the U.S. Crude Oil Imported Acquisition Cost by Refiners. However, there is widespread evidence that the possibility of a global oil market via integration is at best, sample period‐ or regime‐specific, and does not necessarily hold at all points in time (Ji and Fan, , ; Jia et al , ; Bhanja et al , ). Given this, there is no guarantee that the impact of GPRs on WTI or a measure of global oil price can be generalised to other major crude oil prices such as Bonny Light, Brent, Dubai, Organization of the Petroleum Exporting Countries (OPEC) Reference Basket (ORB) and Tapis.…”

Section: Introductionmentioning

confidence: 99%

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Geopolitical risks and the predictability of regional oil returns and volatility

Demirer

Gupta

Ji³

et al. 2019

OPEC Energy Review

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This paper hypothesises that global geopolitical risks (GPRs) can predict oil market return and volatility. For our purpose, we use a k‐th order non‐parametric causality‐in‐quantile test, applied to a daily data set covering the period of 15 May 1996 to 31 May 2018 of six oil benchmarks (the Nigerian Bonny Light, Brent, Dubai, OPEC Reference Basket (ORB), Tapis and WTI). Our results indicate that the relationship between oil returns and GPRs is highly non‐linear, and hence, linear tests of Granger causality cannot be relied upon. Based on the data‐driven econometric method, we observe that GPRs have predictability for oil returns of the West African Bonny Light, ORB and Tapis, while in terms of volatility, causality is observed for all oil prices barring the case of Dubai. In sum, the impact of GPRs is primarily on volatility of oil markets, but more importantly, the impact of GPRs is not uniform across the oil markets.

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Intra-day co-movements of crude oil futures: China and the international benchmarks

Zhang

Zhao

2021

Ann Oper Res

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Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its comovements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases. KeywordsINE Crude oil futures • WTI and Brent • Intra-day co-movement patterns • Complex network model • Pairs trading JEL Classification C12 • C52 • G11 • G15 B Qiang Ji

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Dynamic integration of world oil prices: A reinvestigation of globalisation vs. regionalisation

Cited by 63 publications

References 37 publications

Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

Geopolitical risks and the predictability of regional oil returns and volatility

Intra-day co-movements of crude oil futures: China and the international benchmarks

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