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Intradaily periodicity and volatility spillovers between international stock index futures markets

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  • Chunchi Wu
  • Jinliang Li
  • Wei Zhang

Abstract

This paper examines short‐run information transmission between the U.S. and U.K. markets using the S&P 500 and FTSE 100 index futures. Ultrahighfrequency futures data are employed—which have a number of advantages over the low‐frequency spot data commonly used in previous studies—in establishing that volatility spillovers are in fact bidirectional. The generalized autoregressive conditionally heteroskedastic model (GARCH) is employed to estimate the mean and volatility spillovers of intraday returns. A Fourier flexible function is utilized to filter the intradaily periodic patterns that induce serial correlation in return volatility. It was found that estimates of volatility persistence and speed of information transmission are seriously affected by intradaily periodicity. The bias in parameter estimation is removed by filtering out the intradaily periodic component of the transaction data. Contrary to previous findings, there is evidence of spillovers in volatility between the U.S. and U.K. markets. Results indicate that the volatility of the U.S. market is affected by the most recent volatility surprise in the U.K. market. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:553–585, 2005

Suggested Citation

  • Chunchi Wu & Jinliang Li & Wei Zhang, 2005. "Intradaily periodicity and volatility spillovers between international stock index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(6), pages 553-585, June.
    • Handle: RePEc:wly:jfutmk:v:25:y:2005:i:6:p:553-585
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    1. Benjamin Hippert & André Uhde & Sascha Tobias Wengerek, 2019. "Portfolio benefits of adding corporate credit default swap indices: evidence from North America and Europe," Review of Derivatives Research, Springer, vol. 22(2), pages 203-259, July.
    2. Tao Chen & Kam C. Chan & Haodong Chang, 2022. "Periodicity of trading activity in foreign exchange markets," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 45(2), pages 445-465, June.
    3. Haidong Cai & Shamim Ahmed & Ying Jiang & Xiaoquan Liu, 2020. "The impact of US macroeconomic news announcements on Chinese commodity futures," Quantitative Finance, Taylor & Francis Journals, vol. 20(12), pages 1927-1966, December.
    4. Gkillas, Konstantinos & Konstantatos, Christoforos & Floros, Christos & Tsagkanos, Athanasios, 2021. "Realized volatility spillovers between US spot and futures during ECB news: Evidence from the European sovereign debt crisis," International Review of Financial Analysis, Elsevier, vol. 74(C).
    5. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    6. Hou, Yang & Li, Steven, 2017. "Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets," MPRA Paper 81999, University Library of Munich, Germany.
    7. Banerjee, Ameet Kumar, 2021. "Futures market and the contagion effect of COVID-19 syndrome," Finance Research Letters, Elsevier, vol. 43(C).
    8. Kuan-Min Wang & Yuan-Ming Lee, 2023. "Are life insurance futures a safe haven during COVID-19?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    9. Tobias E Anheluk & Pradosh Simlai, 2011. "Information spillovers between size and value premium in average stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 12(6), pages 395-406, December.
    10. Antonakakis, Nikolaos & Floros, Christos & Kizys, Renatas, 2016. "Dynamic spillover effects in futures markets: UK and US evidence," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 406-418.
    11. Hou, Yang & Li, Steven & Wen, Fenghua, 2019. "Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach," Energy Economics, Elsevier, vol. 83(C), pages 119-143.
    12. Yang Hou & Steven Li & Fenghua Wen, 2021. "Time-varying information share and autoregressive loading factors: evidence from S&P 500 cash and E-mini futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 91-110, July.
    13. Chao Liang & Yu Wei & Yaojie Zhang, 2020. "Is implied volatility more informative for forecasting realized volatility: An international perspective," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1253-1276, December.
    14. Chao Liang & Yi Zhang & Yaojie Zhang, 2022. "Forecasting the volatility of the German stock market: New evidence," Applied Economics, Taylor & Francis Journals, vol. 54(9), pages 1055-1070, February.
    15. Hou, Yang & Li, Steven, 2016. "Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach," Economic Modelling, Elsevier, vol. 52(PB), pages 884-897.
    16. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    17. Roland Füss & Ferdinand Mager & Michael Stein & Lu Zhao, 2018. "Financial crises, price discovery, and information transmission: a high-frequency perspective," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(4), pages 333-365, November.
    18. Chen, Xiangyu & Tongurai, Jittima, 2022. "Spillovers and interdependency across base metals: Evidence from China's futures and spot markets," Resources Policy, Elsevier, vol. 75(C).
    19. Konstantinos Tsiaras, 2020. "Contagion in Futures Metal Markets during the Recent Global Financial Crisis: Evidence from Gold, Silver, Copper, Zinc and Aluminium," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 70(3-4), pages 42-55, July-Dece.
    20. Kung, Ling-Ming & Yu, Shang-Wu, 2008. "Prediction of index futures returns and the analysis of financial spillovers--A comparison between GARCH and the grey theorem," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1184-1200, May.
    21. Anagnostidis, Panagiotis & Emmanouilides, Christos J., 2015. "Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 473-487.
    22. Konstantinos Gkillas & Christoforos Konstantatos & Costas Siriopoulos, 2021. "Uncertainty Due to Infectious Diseases and Stock–Bond Correlation," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    23. Young‐Rae Song & Yong‐Jun Yang & Hyung‐Sik Oh, 2009. "Interaction between Foreign and Domestic Investors in the Korean Stock and Futures Markets," Asian Economic Journal, East Asian Economic Association, vol. 23(2), pages 249-267, June.
    24. Mateus, Cesario & Chinthalapati, Raju & Mateus, Irina B., 2017. "Intraday industry-specific spillover effect in European equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 278-298.

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