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Modeling intraday volatility of European bond markets: A data filtering application

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  • Zhang, Hanyu
  • Dufour, Alfonso

Abstract

This paper studies the intraday volatility of European government bonds under the framework of the multiplicative component GARCH model (Engle and Sokalska, 2012). Intraday return volatility is specified as the product of daily volatility, intraday seasonality, and a unit GARCH process. The model is applied to 10-year European government bonds during the sovereign debt crisis. We observe large transitory intraday volatility often due to illiquidity effects and outliers. We suggest a flexible and effective procedure for jointly filtering mid-quote prices and estimating volatility models. Finally, we show that intraday data contain relevant information for daily volatility forecasts.

Suggested Citation

  • Zhang, Hanyu & Dufour, Alfonso, 2019. "Modeling intraday volatility of European bond markets: A data filtering application," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 131-146.
  • Handle: RePEc:eee:finana:v:63:y:2019:i:c:p:131-146
    DOI: 10.1016/j.irfa.2019.02.002
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    as
    1. Cheung, Yiu Chung & de Jong, Frank & Rindi, Barbara, 2005. "Trading European sovereign bonds: the microstructure of the MTS trading platforms," Working Paper Series 432, European Central Bank.
    2. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    3. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    4. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    5. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    6. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    7. Michael J. Fleming & Jose A. Lopez, 1999. "Heat waves, meteor showers, and trading volume: an analysis of volatility spillovers in the U.S. Treasury market," Working Papers in Applied Economic Theory 99-09, Federal Reserve Bank of San Francisco.
    8. Michael J. Fleming, 2003. "Measuring treasury market liquidity," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 83-108.
    9. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    10. De Bruyckere, Valerie & Gerhardt, Maria & Schepens, Glenn & Vander Vennet, Rudi, 2013. "Bank/sovereign risk spillovers in the European debt crisis," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4793-4809.
    11. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    12. Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
    13. Peter G. Dunne & Michael J. Moore & Richard Portes, 2007. "Benchmark Status in Fixed‐Income Asset Markets," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(9‐10), pages 1615-1634, November.
    14. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    15. Robert F. Engle & Magdalena E. Sokalska, 0. "Forecasting intraday volatility in the US equity market. Multiplicative component GARCH," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(1), pages 54-83.
    16. Brownlees, C.T. & Gallo, G.M., 2006. "Financial econometric analysis at ultra-high frequency: Data handling concerns," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2232-2245, December.
    17. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    18. 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.
    19. Alessandro Beber & Michael W. Brandt & Kenneth A. Kavajecz, 2009. "Flight-to-Quality or Flight-to-Liquidity? Evidence from the Euro-Area Bond Market," Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 925-957.
    20. Pasquariello, Paolo & Vega, Clara, 2009. "The on-the-run liquidity phenomenon," Journal of Financial Economics, Elsevier, vol. 92(1), pages 1-24, April.
    21. Chou, Ray Yeutien, 1988. "Volatility Persistence and Stock Valuations: Some Empirical Evidence Using Garch," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(4), pages 279-294, October-D.
    22. Eric Ghysels & Julien Idier & Simone Manganelli & Olivier Vergote, 2017. "A High-Frequency assessment of the ECB Securities Markets Programme," Journal of the European Economic Association, European Economic Association, vol. 15(1), pages 218-243.
    23. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    24. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
    25. de Goeij, P. C. & Marquering, W., 2004. "Modeling the conditional covariance between stock and bond returns : A multivariate GARCH approach," Other publications TiSEM 94fe5ada-715a-4339-b94c-f, Tilburg University, School of Economics and Management.
    26. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    27. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    28. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    29. F. M. Bandi & J. R. Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," Review of Economic Studies, Oxford University Press, vol. 75(2), pages 339-369.
    30. Dimitriou, Dimitrios & Kenourgios, Dimitris & Simos, Theodore, 2013. "Global financial crisis and emerging stock market contagion: A multivariate FIAPARCH–DCC approach," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 46-56.
    31. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    32. Hasbrouck, Joel, 2018. "High-Frequency Quoting: Short-Term Volatility in Bids and Offers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(2), pages 613-641, April.
    33. Diaz, Antonio & Merrick, John Jr. & Navarro, Eliseo, 2006. "Spanish Treasury bond market liquidity and volatility pre- and post-European Monetary Union," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1309-1332, April.
    34. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    35. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    36. Peter de Goeij, 2004. "Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate GARCH Approach," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(4), pages 531-564.
    37. Blume, Marshall E & Keim, Donald B & Patel, Sandeep A, 1991. "Returns and Volatility of Low-Grade Bonds: 1977-1989," Journal of Finance, American Finance Association, vol. 46(1), pages 49-74, March.
    38. Peter G. Dunne & Michael J. Moore & Richard Portes, 2007. "Benchmark Status in Fixed‐Income Asset Markets," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(9‐10), pages 1615-1634, November.
    39. Taylor, Stephen J. & Xu, Xinzhong, 1997. "The incremental volatility information in one million foreign exchange quotations," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 317-340, December.
    40. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    41. Ehrmann, Michael & Fratzscher, Marcel, 2017. "Euro area government bonds – Fragmentation and contagion during the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 26-44.
    42. Charlotte Christiansen, 2007. "Volatility‐Spillover Effects in European Bond Markets," European Financial Management, European Financial Management Association, vol. 13(5), pages 923-948, November.
    43. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
    44. Hasbrouck, Joel, 1993. "Assessing the Quality of a Security Market: A New Approach to Transaction-Cost Measurement," Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 191-212.
    45. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    46. Torben G. Andersen & Tim Bollerslev, 1998. "Deutsche Mark-Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies," Journal of Finance, American Finance Association, vol. 53(1), pages 219-265, February.
    47. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
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    Cited by:

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    3. Sensoy, Ahmet & Serdengeçti, Süleyman, 2020. "Impact of portfolio flows and heterogeneous expectations on FX jumps: Evidence from an emerging market," International Review of Financial Analysis, Elsevier, vol. 68(C).
    4. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Estimating yield spreads volatility using GARCH-type models," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    5. Kin-Boon Tang & Shao-Jye Wong & Shih-Kuei Lin & Szu-Lang Liao, 2020. "Excess volatility and market efficiency in government bond markets: the ASEAN-5 context," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 154-165, March.
    6. Baker, H. Kent & Kumar, Satish & Goyal, Kirti & Sharma, Anuj, 2021. "International review of financial analysis: A retrospective evaluation between 1992 and 2020," International Review of Financial Analysis, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    Intraday GARCH; European Bond Markets; Data Filters;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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