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Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China

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  • Jian, Zhihong
  • Li, Xupei
  • Zhu, Zhican

Abstract

We develop a skewness-dependent multivariate conditional autoregressive value at risk model (SDMV-CAViaR) to detect the extreme risk transmission channels between the Chinese stock index futures and spot markets. The proposed SDMV-CAViaR model improves the forecast performance of extreme risk by introducing the high-frequency realized skewness. Specifically, the realized skewness has a significant impact on the spillovers, but the realized volatility and realized kurtosis do not, which implies that the jump component plays an important role in extreme risk spillovers. The empirical results indicate there are bidirectional extreme risk spillovers between the stock index futures and spot markets, the decline of one market has direct and indirect channels to exacerbate the extreme risk of the other market. Firstly, the market decline will directly increase the extreme risk of related markets by decreasing market returns. Besides, the decline will indirectly increase the extreme risk by increasing the negative realized skewness and extreme risk spillovers.

Suggested Citation

  • Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2022. "Extreme risk transmission channels between the stock index futures and spot markets: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:ecofin:v:59:y:2022:i:c:s1062940821002242
    DOI: 10.1016/j.najef.2021.101632
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    as
    1. Natalia Nolde & Johanna F. Ziegel, 2016. "Elicitability and backtesting: Perspectives for banking regulation," Papers 1608.05498, arXiv.org, revised Feb 2017.
    2. Yu‐Lun Chen & Yin‐Feng Gau, 2009. "Tick sizes and relative rates of price discovery in stock, futures, and options markets: Evidence from the Taiwan stock exchange," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(1), pages 74-93, January.
    3. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    4. Jian Yang & Zihui Yang & Yinggang Zhou, 2012. "Intraday price discovery and volatility transmission in stock index and stock index futures markets: Evidence from China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(2), pages 99-121, February.
    5. Olivier J. Blanchard & Mark W. Watson, 1982. "Bubbles, Rational Expectations and Financial Markets," NBER Working Papers 0945, National Bureau of Economic Research, Inc.
    6. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    7. Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2018. "Gold futures returns and realized moments: A forecasting experiment using a quantile-boosting approach," Resources Policy, Elsevier, vol. 57(C), pages 196-212.
    8. Robert F. Engle & Magdalena E. Sokalska, 0. "Forecasting intraday volatility in the US equity market. Multiplicative component GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 10(1), pages 54-83.
    9. Asim Ghosh, 1993. "Cointegration and error correction models: Intertemporal causality between index and futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(2), pages 193-198, April.
    10. Pratap Chandra Pati & Prabina Rajib, 2011. "Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures," Applied Economics Letters, Taylor & Francis Journals, vol. 18(6), pages 567-574.
    11. Jian, Zhihong & Wu, Shuai & Zhu, Zhican, 2018. "Asymmetric extreme risk spillovers between the Chinese stock market and index futures market: An MV-CAViaR based intraday CoVaR approach," Emerging Markets Review, Elsevier, vol. 37(C), pages 98-113.
    12. Sibley, Steven E. & Wang, Yanchu & Xing, Yuhang & Zhang, Xiaoyan, 2016. "The information content of the sentiment index," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 164-179.
    13. McInish, Thomas H & Wood, Robert A, 1992. "An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks," Journal of Finance, American Finance Association, vol. 47(2), pages 753-764, June.
    14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    15. Lucian Streche, 2009. "Lead – Lag Relationship between the Romanian Cash Market and Futures Market," Advances in Economic and Financial Research - DOFIN Working Paper Series 26, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    16. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    17. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    18. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2016. "Why Invest in Emerging Markets? The Role of Conditional Return Asymmetry," Journal of Finance, American Finance Association, vol. 71(5), pages 2145-2192, October.
    19. Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
      • Tobias Adrian & Markus K. Brunnermeier, 2011. "CoVaR," NBER Working Papers 17454, National Bureau of Economic Research, Inc.
    20. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    21. Efraim Benmelech & Eugene Kandel & Pietro Veronesi, 2010. "Stock-Based Compensation and CEO (Dis)Incentives," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(4), pages 1769-1820.
    22. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    23. Chan, Kalok & Chan, K C & Karolyi, G Andrew, 1991. "Intraday Volatility in the Stock Index and Stock Index Futures Markets," The Review of Financial Studies, Society for Financial Studies, vol. 4(4), pages 657-684.
    24. Berger, Dave & Pukthuanthong, Kuntara, 2012. "Market fragility and international market crashes," Journal of Financial Economics, Elsevier, vol. 105(3), pages 565-580.
    25. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    26. Girardi, Giulio & Tolga Ergün, A., 2013. "Systemic risk measurement: Multivariate GARCH estimation of CoVaR," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3169-3180.
    27. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    28. Wee Ching Pok & Sunil Poshakwale, 2004. "The impact of the introduction of futures contracts on the spot market volatility: the case of Kuala Lumpur Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 14(2), pages 143-154.
    29. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
    30. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2016. "Exploiting the errors: A simple approach for improved volatility forecasting," Journal of Econometrics, Elsevier, vol. 192(1), pages 1-18.
    31. Tim Bollerslev & Jia Li & Yuan Xue, 2018. "Volume, Volatility, and Public News Announcements," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2005-2041.
    32. Qian Han & Jufang Liang, 2017. "Index Futures Trading Restrictions and Spot Market Quality: Evidence from the Recent Chinese Stock Market Crash," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(4), pages 411-428, April.
    33. Chang, Xin & Chen, Yangyang & Zolotoy, Leon, 2017. "Stock Liquidity and Stock Price Crash Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1605-1637, August.
    34. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    35. Reboredo, Juan C. & Rivera-Castro, Miguel A. & Ugolini, Andrea, 2016. "Downside and upside risk spillovers between exchange rates and stock prices," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 76-96.
    36. Pierluigi Bologna & Laura Cavallo, 2002. "Does the introduction of stock index futures effectively reduce stock market volatility? Is the 'futures effect' immediate? Evidence from the Italian stock exchange using GARCH," Applied Financial Economics, Taylor & Francis Journals, vol. 12(3), pages 183-192.
    37. Tobias Fissler & Johanna F. Ziegel & Tilmann Gneiting, 2015. "Expected Shortfall is jointly elicitable with Value at Risk - Implications for backtesting," Papers 1507.00244, arXiv.org, revised Jul 2015.
    38. Hou, Yang & Nartea, Gilbert, 2017. "Price Discovery in the Stock Index Futures Market: Evidence from the Chinese stock market crash," MPRA Paper 81995, University Library of Munich, Germany.
    39. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
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    More about this item

    Keywords

    SDMV-CAViaR model; Realized skewness; Extreme risk spillovers;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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