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Oil price risk exposure of BRIC stock markets and hedging effectiveness

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Abstract

We study the tail dependence between crude oil and BRIC stock markets using a time-varying optimal copula (TVOC) approach. We show evidence of multiple tail dependence regimes, suggesting that simple static or dynamic copula specifications do not fully characterize the extreme dependence between oil and BRIC stock markets. The identified combinations of asymmetric and extreme positive lower tail dependence justify the application of the TVOC. Interestingly, the positive lower tail dependence between oil and stock markets and risk spillover from oil is higher for Brazil and Russia (oil exporters) than India and China (oil importers). Finally, we assess the effectiveness of hedging and measure the conditional diversification benefits of investing in oil for BRIC stock indices. Notably, the Chinese and Indian equity markets offer higher conditional diversification benefits when combined with oil in an equally weighted portfolio.

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Notes

  1. Crude oil prices can also move international bond markets (e.g., Nazlioglu et al., 2020).

  2. Some studies (e.g., Zhu et al., 2014; Cheema and Scrimgeour, 2019) report a positive impact of oil prices on stock returns of emerging economies, arguing that increasing oil prices are perceived by investors as a signal of stronger business performance.

  3. Brazil is considered a rising power in terms of economic growth and political influence beyond South America.

  4. For example, Fang and Egan (2018) indicate that “investors holding a portfolio of oil and Chinese stocks should pay special attention to the extreme changes in crude oil prices and adopt hedging measures to protect their portfolio from extreme shocks to oil markets”.

  5. Some recent studies apply not only the common copulas but also half-rotated copulas in order to capture negative market dependence in extreme cases (e.g., Patton, 2012; Reboredo and Ugolini, 2015). However, the TVOC is more advantageous, as it combines all these copulas together to capture potential changes in the type of tail dependence between markets over time.

  6. According to Liu et al. (2017), the Normal and Student-t copulas can capture both positive and negative dependence between markets.

  7. The details of the distribution-free test can be seen in Liu et al. (2016).

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Authors and Affiliations

  1. Montpellier Business School, University of Montpellier, Montpellier Research in Management, Montpellier, France

    Syed Jawad Hussain Shahzad

  2. South Ural State University, 76, Lenin Prospekt, Chelyabinsk, Russian Federation

    Syed Jawad Hussain Shahzad & Mobeen Ur Rehman

  3. Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

    Elie Bouri

  4. UCD College of Business, University College Dublin, Dublin, Ireland

    Muhammad Abubakr Naeem

  5. Nonlinear Analysis and Applied Mathematics (NAAM)-Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

    Tareq Saeed

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  1. Syed Jawad Hussain Shahzad
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Shahzad, S.J.H., Bouri, E., Rehman, M.U. et al. Oil price risk exposure of BRIC stock markets and hedging effectiveness. Ann Oper Res 313, 145–170 (2022). https://doi.org/10.1007/s10479-021-04078-0

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