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Connectedness in International Crude Oil Markets

Author

Listed:
  • Niyati Bhanja

    (MICA - The School of Ideas)

  • Samia Nasreen

    (Lahore College for Women University)

  • Arif Billah Dar

    (Shri Mata Vaishno Devi University (SMVDU))

  • Aviral Kumar Tiwari

    (Rajagiri Business School
    South Ural State University)

Abstract

This paper proposes study framework that investigates the globalization–regionalization debate using both the time domain and frequency domain measures of connectedness. Seven international global crude oil benchmarks are used to analyse whether crude oil market is globalized or regionalized. To this end, we first utilize the Bayesian inference of dynamic correlation in multivariate factor stochastic framework proposed by Kastner et al. (Sparse Bayesian time-varying covariance estimation many dimensions, 2017. arXiv:1608.08468 ). Next, we employ the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Barunik and Krehlik (J Financ Econom, 2018. http://doi.org/10.1093/jjfinec/nby001Barunik ) spillover indices to analyse the connectedness among the set of oil prices under consideration. The period of the study is 15/05/1996 to 07/03/2018. The dynamic correlation results reveal persistent correlation between different pairs of crude oil market over the whole sample period. The overall connectedness results indicate that crude oil market is well integrated. The volatility spillover results show that Forcado is the most affected by shocks from other markets. The return series of Brent at all frequencies appear to be the main source of volatility transmission in crude oil market. The results of overall connectedness show that connectedness is similar across frequencies. These findings are very important from the perspective of understanding the connectedness of global oil market.

Suggested Citation

  • Niyati Bhanja & Samia Nasreen & Arif Billah Dar & Aviral Kumar Tiwari, 2022. "Connectedness in International Crude Oil Markets," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 227-262, January.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10068-4
    DOI: 10.1007/s10614-020-10068-4
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    More about this item

    Keywords

    Time–frequency domain connectedness; Network analysis; Crude oil market; Dynamic correlation;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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