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The effect of oil returns on the stock markets network

Author

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  • Han, Liyan
  • Lv, Qiuna
  • Yin, Libo

Abstract

In this paper, we focus on analyzing the impact of oil price shocks on the interactions of oil-stock prices during the period 2012–2018 using graph theory. We first construct 1622 successive daily oil-stock networks to model the oil-stock price interactions, where the nodes represent distinct stocks and the links measure the cross-correlations among the fluctuations of respective stock prices. The dynamic connectivity changes of the daily oil-stock networks are then captured by three network-based indicators: network density, network efficiency and network entropy. To evaluate the impact of oil price shocks on the three indicators, we consider different linear and nonlinear specifications. The evidence confirms a significantly negative impact of oil price shocks on the network indicators from the in-sample and out-of-sample perspectives. That is, with the decrease in oil returns, the interactions among the oil-related stocks strengthen, and correspondingly, the price transmission efficiency improves and the oil-stock market becomes more homogeneous, which indicate that fluctuations in oil prices can be a signal for portfolio diversification in stock markets, and vice versa. The asymmetric effect shows that negative changes in oil prices have a more significant impact than positive variations on the stock market. Further evidence suggests that the linear model is more successful in detecting oil shocks to the linkages of stock networks than the regime-switching model.

Suggested Citation

  • Han, Liyan & Lv, Qiuna & Yin, Libo, 2019. "The effect of oil returns on the stock markets network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    • Handle: RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311811
    DOI: 10.1016/j.physa.2019.122044
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    Citations

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    Cited by:

    1. Xu, Xin & Huang, Shupei & An, Haizhong, 2022. "The dynamic moderating function of the exchange rate market on the oil-stock nexus," International Review of Financial Analysis, Elsevier, vol. 81(C).
    2. Mahsa Ghorbani & Edwin K P Chong, 2020. "Stock price prediction using principal components," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
    3. Huang, Jionghao & Li, Ziruo & Xia, Xiaohua, 2021. "Network diffusion of international oil volatility risk in China's stock market: Quantile interconnectedness modelling and shock decomposition analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 1-39.
    4. Chen, Weidong & Xiong, Shi & Chen, Quanyu, 2022. "Characterizing the dynamic evolutionary behavior of multivariate price movement fluctuation in the carbon-fuel energy markets system from complex network perspective," Energy, Elsevier, vol. 239(PA).

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