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Analyzing quantile spillover effects among international financial markets

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  • Wang, Jie
  • Liu, Tangyong
  • Pan, Na

Abstract

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.

Suggested Citation

  • Wang, Jie & Liu, Tangyong & Pan, Na, 2023. "Analyzing quantile spillover effects among international financial markets," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940823000049
    DOI: 10.1016/j.najef.2023.101881
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    as
    1. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    2. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    3. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    4. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    5. John Wei, K. C. & Liu, Yu-Jane & Yang, Chau-Chen & Chaung, Guey-Shiang, 1995. "Volatility and price change spillover effects across the developed and emerging markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(1), pages 113-136, May.
    6. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    7. Alter, Adrian & Beyer, Andreas, 2014. "The dynamics of spillover effects during the European sovereign debt turmoil," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 134-153.
    8. Mylonidis, Nikolaos & Kollias, Christos, 2010. "Dynamic European stock market convergence: Evidence from rolling cointegration analysis in the first euro-decade," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2056-2064, September.
    9. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    10. Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
    11. Bessler, David A. & Yang, Jian, 2003. "The structure of interdependence in international stock markets," Journal of International Money and Finance, Elsevier, vol. 22(2), pages 261-287, April.
    12. Tangyong Liu & Xu Gong & Boqiang Lin, 2021. "Analyzing the frequency dynamics of volatility spillovers across precious and industrial metal markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1375-1396, September.
    13. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    14. Bollerslev, Tim & Todorov, Viktor & Li, Sophia Zhengzi, 2013. "Jump tails, extreme dependencies, and the distribution of stock returns," Journal of Econometrics, Elsevier, vol. 172(2), pages 307-324.
    15. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    16. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    17. Kasa, Kenneth, 1992. "Common stochastic trends in international stock markets," Journal of Monetary Economics, Elsevier, vol. 29(1), pages 95-124, February.
    18. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    19. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
    20. Adler, Michael & Dumas, Bernard, 1983. "International Portfolio Choice and Corporation Finance: A Synthesis," Journal of Finance, American Finance Association, vol. 38(3), pages 925-984, June.
    21. Tang, Wenjin & Ding, Saijie & Chen, Hao, 2021. "Economic uncertainty and its spillover networks: Evidence from the Asia-Pacific countries," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    22. Dean, Warren G. & Faff, Robert W. & Loudon, Geoffrey F., 2010. "Asymmetry in return and volatility spillover between equity and bond markets in Australia," Pacific-Basin Finance Journal, Elsevier, vol. 18(3), pages 272-289, June.
    23. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    24. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
    25. McQueen, Grant & Roley, V Vance, 1993. "Stock Prices, News, and Business Conditions," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 683-707.
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    Cited by:

    1. Zhongzheng, Wang, 2023. "Extreme risk transmission mechanism between oil, green bonds and new energy vehicles," Innovation and Green Development, Elsevier, vol. 2(3).

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    More about this item

    Keywords

    Quantile spillovers; Quantile vector autoregressive model; Total spillover index; Net spillover index; Asymmetry;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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