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Tests for jumps in yield spreads

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  • Winkelmann, Lars
  • Yao, Wenying

Abstract

This paper develops high-frequency econometric methods to test for jumps in the spread of bond yields. We derive a coherent inference procedure that detects a jump in the yield spread only if at least one of the two underlying bonds displays a jump. We formalize the test as a sequential procedure in the context of an intersection union test in multiple testing and introduce a new bivariate jump test for pre-averaged intra-day returns. In an empirical application involving high-frequency data of U.S. government bonds, we contrast response patterns of term spreads and break-even in ation across monetary policy announcements, in ation, and employment news releases.

Suggested Citation

  • Winkelmann, Lars & Yao, Wenying, 2021. "Tests for jumps in yield spreads," Discussion Papers 2021/15, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:202115
    DOI: 10.17169/refubium-32211
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    References listed on IDEAS

    as
    1. Ana-Maria Dumitru & Giovanni Urga, 2011. "Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 242-255, October.
    2. Bibinger, Markus & Neely, Christopher & Winkelmann, Lars, 2019. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Journal of Econometrics, Elsevier, vol. 209(2), pages 158-184.
    3. Lubos Pástor & Pietro Veronesi, 2012. "Uncertainty about Government Policy and Stock Prices," Journal of Finance, American Finance Association, vol. 67(4), pages 1219-1264, August.
    4. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
    5. Lee, Suzanne S. & Mykland, Per A., 2012. "Jumps in equilibrium prices and market microstructure noise," Journal of Econometrics, Elsevier, vol. 168(2), pages 396-406.
    6. Yacine Ait-Sahalia & Jialin Yu, 2008. "High Frequency Market Microstructure Noise Estimates and Liquidity Measures," NBER Working Papers 13825, National Bureau of Economic Research, Inc.
    7. Bibinger, Markus & Winkelmann, Lars, 2015. "Econometrics of co-jumps in high-frequency data with noise," Journal of Econometrics, Elsevier, vol. 184(2), pages 361-378.
    8. Jérôme Lahaye & Sébastien Laurent & Christopher J. Neely, 2011. "Jumps, cojumps and macro announcements," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 893-921, September.
    9. Peter Hördahl & Eli M. Remolona & Giorgio Valente, 2020. "Expectations and Risk Premia at 8:30 a.m.: Deciphering the Responses of Bond Yields to Macroeconomic Announcements," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 27-42, January.
    10. Christensen, Kim & Kinnebrock, Silja & Podolskij, Mark, 2010. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," Journal of Econometrics, Elsevier, vol. 159(1), pages 116-133, November.
    11. Lars Winkelmann & Markus Bibinger & Tobias Linzert, 2016. "ECB Monetary Policy Surprises: Identification Through Cojumps in Interest Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 613-629, June.
    12. Chernov, Mikhail & Mueller, Philippe, 2012. "The term structure of inflation expectations," Journal of Financial Economics, Elsevier, vol. 106(2), pages 367-394.
    13. Aït-Sahalia, Yacine & Jacod, Jean & Li, Jia, 2012. "Testing for jumps in noisy high frequency data," Journal of Econometrics, Elsevier, vol. 168(2), pages 207-222.
    14. Leombroni, Matteo & Vedolin, Andrea & Venter, Gyuri & Whelan, Paul, 2021. "Central bank communication and the yield curve," Journal of Financial Economics, Elsevier, vol. 141(3), pages 860-880.
    15. Jiang, George J. & Lo, Ingrid & Verdelhan, Adrien, 2011. "Information Shocks, Liquidity Shocks, Jumps, and Price Discovery: Evidence from the U.S. Treasury Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(2), pages 527-551, April.
    16. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    17. Koike, Yuta, 2016. "Estimation Of Integrated Covariances In The Simultaneous Presence Of Nonsynchronicity, Microstructure Noise And Jumps," Econometric Theory, Cambridge University Press, vol. 32(3), pages 533-611, June.
    18. Hanson, Samuel G. & Stein, Jeremy C., 2015. "Monetary policy and long-term real rates," Journal of Financial Economics, Elsevier, vol. 115(3), pages 429-448.
    19. Swanson, Eric T., 2021. "Measuring the effects of federal reserve forward guidance and asset purchases on financial markets," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 32-53.
    20. repec:taf:jnlbes:v:30:y:2012:i:2:p:242-255 is not listed on IDEAS
    21. 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.
    22. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    23. Parley Ruogu Yang, 2020. "Using the yield curve to forecast economic growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1057-1080, November.
    24. Aït-Sahalia, Yacine & Xiu, Dacheng, 2019. "A Hausman test for the presence of market microstructure noise in high frequency data," Journal of Econometrics, Elsevier, vol. 211(1), pages 176-205.
    25. repec:hal:journl:peer-00732537 is not listed on IDEAS
    26. Suzanne S. Lee, 2012. "Jumps and Information Flow in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 25(2), pages 439-479.
    27. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    28. Todd Henry & Peter C.B. Phillips, 2020. "Forecasting Economic Activity Using the Yield Curve: Quasi-Real-Time Applications for New Zealand, Australia and the US," Cowles Foundation Discussion Papers 2259, Cowles Foundation for Research in Economics, Yale University.
    29. Darrell Duffie & Rui Kan, 1996. "A Yield‐Factor Model Of Interest Rates," Mathematical Finance, Wiley Blackwell, vol. 6(4), pages 379-406, October.
    30. M. Podolskij & D. Ziggel, 2010. "New tests for jumps in semimartingale models," Statistical Inference for Stochastic Processes, Springer, vol. 13(1), pages 15-41, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    High-frequency data; sequential testing; news announcements; term spread; break-even inflation;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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