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Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

Abstract

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

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Notes

  1. 1.

    Construction of wavelet details via MODWT and wavelet cross-correlations are calculated by using an original code in the ‘waveslim’ package in ‘R’ software.

  2. 2.

    Besides wavelet cross-corelation, there are other similar concepts in wavelet analysis, as for example coherency or the wavelet correlation (see e.g. Rua 2010).

  3. 3.

    Estimation of GARCH-normal, GARCH-st, GARCH-ged and GARCH-gat models was done via ‘GEVStableGarch’ package in ‘R’ software.

  4. 4.

    Estimation of robust quantile regression was done via ‘lqr’ package in ‘R’ software.

  5. 5.

    http://www.imf.org/external/pubs/cat/longres.aspx?sk=43343.01.

  6. 6.

    For the construction of conditional volatilities for WTI, we apply the same procedure as in the case of Brent. Acording to AIC values, the optimal model for D1 and D2 scales is GARCH-gat, and for D3 scale it is GARCH-ged.

  7. 7.

    These AIC results can be obtained by request.

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Correspondence to Dejan Živkov.

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Živkov, D., Manić, S., Kovačević, J. et al. Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression. Port Econ J (2020). https://doi.org/10.1007/s10258-020-00189-x

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Keywords

  • Volatility spillover effect
  • Oil and stock markets
  • Wavelets
  • Robust quantile regression

JEL codes

  • C14
  • C63
  • G12
  • Q02