IDEAS home Printed from https://ideas.repec.org/p/fip/fedlwp/97969.html
   My bibliography  Save this paper

Sluggish news reactions: A combinatorial approach for synchronizing stock jumps

Author

Listed:

Abstract

Stock prices often react sluggishly to news, producing gradual jumps and jump delays. Econometricians typically treat these sluggish reactions as microstructure effects and settle for a coarse sampling grid to guard against them. Synchronizing mistimed stock returns on a fine sampling grid allows us to better approximate the true common jumps in related stock prices.

Suggested Citation

  • Nabil Bouamara & Kris Boudt & Sebastien Laurent & Christopher J. Neely, 2024. "Sluggish news reactions: A combinatorial approach for synchronizing stock jumps," Working Papers 2024-006, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:97969
    DOI: 10.20955/wp.2024.006
    as

    Download full text from publisher

    File URL: https://s3.amazonaws.com/real.stlouisfed.org/wp/2024/2024-006.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.20955/wp.2024.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:hal:journl:peer-00815564 is not listed on IDEAS
    2. Z. Merrick Li & Oliver Linton, 2022. "A ReMeDI for Microstructure Noise," Econometrica, Econometric Society, vol. 90(1), pages 367-389, January.
    3. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics," Econometrica, Econometric Society, vol. 72(3), pages 885-925, May.
    6. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    7. 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.
    8. Li, Jia & Todorov, Viktor & Tauchen, George & Chen, Rui, 2017. "Mixed-scale jump regressions with bootstrap inference," Journal of Econometrics, Elsevier, vol. 201(2), pages 417-432.
    9. 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.
    10. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    11. Ardia, David & Boudt, Kris, 2015. "Testing equality of modified Sharpe ratios," Finance Research Letters, Elsevier, vol. 13(C), pages 97-104.
    12. Yacine Aït-Sahalia & Jean Jacod, 2014. "High-Frequency Financial Econometrics," Economics Books, Princeton University Press, edition 1, number 10261.
    13. Jia Li & Viktor Todorov & George Tauchen & Huidi Lin, 2019. "Rank Tests at Jump Events," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 312-321, April.
    14. Carole Bernard & Oleg Bondarenko & Steven Vanduffel, 2018. "Rearrangement algorithm and maximum entropy," Annals of Operations Research, Springer, vol. 261(1), pages 107-134, February.
    15. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    16. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    17. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel, 2017. "Value-at-Risk Bounds With Variance Constraints," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(3), pages 923-959, September.
    18. Kris Boudt & Edgars Jakobsons & Steven Vanduffel, 2018. "Block rearranging elements within matrix columns to minimize the variability of the row sums," 4OR, Springer, vol. 16(1), pages 31-50, March.
    19. Boudt, Kris & Laurent, Sébastien & Lunde, Asger & Quaedvlieg, Rogier & Sauri, Orimar, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Journal of Econometrics, Elsevier, vol. 196(2), pages 347-367.
    20. Embrechts, Paul & Puccetti, Giovanni & Rüschendorf, Ludger, 2013. "Model uncertainty and VaR aggregation," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 2750-2764.
    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," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Cambridge Working Papers in Economics 2449, Faculty of Economics, University of Cambridge.
    2. Bo Yu & Bruce Mizrach & Norman R. Swanson, 2020. "New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section," Econometrics, MDPI, vol. 8(2), pages 1-52, May.
    3. Zhao, X. & Hong, S. Y. & Linton, O. B., 2024. "Jumps Versus Bursts: Dissection and Origins via a New Endogenous Thresholding Approach," Janeway Institute Working Papers 2423, Faculty of Economics, University of Cambridge.
    4. Prosper Dovonon & Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2019. "Bootstrapping High-Frequency Jump Tests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 793-803, April.
    5. 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.
    6. Gilder, Dudley & Shackleton, Mark B. & Taylor, Stephen J., 2014. "Cojumps in stock prices: Empirical evidence," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 443-459.
    7. Barunik, Jozef & Vacha, Lukas, 2018. "Do co-jumps impact correlations in currency markets?," Journal of Financial Markets, Elsevier, vol. 37(C), pages 97-119.
    8. Dungey, Mardi & Erdemlioglu, Deniz & Matei, Marius & Yang, Xiye, 2018. "Testing for mutually exciting jumps and financial flights in high frequency data," Journal of Econometrics, Elsevier, vol. 202(1), pages 18-44.
    9. Anabelle Couleau & Teresa Serra & Philip Garcia, 2020. "Are Corn Futures Prices Getting “Jumpy”?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 569-588, March.
    10. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    11. Arouri, Mohamed & M’saddek, Oussama & Nguyen, Duc Khuong & Pukthuanthong, Kuntara, 2019. "Cojumps and asset allocation in international equity markets," Journal of Economic Dynamics and Control, Elsevier, vol. 98(C), pages 1-22.
    12. Lars Winkelmann & Wenying Yao, 2024. "Tests for Jumps in Yield Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 946-957, July.
    13. Grønborg, Niels S. & Lunde, Asger & Olesen, Kasper V. & Vander Elst, Harry, 2022. "Realizing correlations across asset classes," Journal of Financial Markets, Elsevier, vol. 59(PA).
    14. Yeh, Jin-Huei & Yun, Mu-Shu, 2023. "Assessing jump and cojumps in financial asset returns with applications in futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    15. Todorov, Viktor & Bollerslev, Tim, 2010. "Jumps and betas: A new framework for disentangling and estimating systematic risks," Journal of Econometrics, Elsevier, vol. 157(2), pages 220-235, August.
    16. Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2017. "Systemic co-jumps," Journal of Financial Economics, Elsevier, vol. 126(3), pages 563-591.
    17. Zhang, Chuanhai & Liu, Zhi & Liu, Qiang, 2021. "Jumps at ultra-high frequency: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    18. Markus Bibinger & Nikolaus Hautsch & Alexander Ristig, 2024. "Jump detection in high-frequency order prices," Papers 2403.00819, arXiv.org.
    19. Pierre Bajgrowicz & Olivier Scaillet & Adrien Treccani, 2016. "Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News," Management Science, INFORMS, vol. 62(8), pages 2198-2217, August.
    20. Caporin, Massimiliano & Kolokolov, Aleksey & Renò, Roberto, 2014. "Multi-jumps," MPRA Paper 58175, University Library of Munich, Germany.

    More about this item

    Keywords

    asynchronicity; cojumps; high-frequency data; microstructure noise; realized covariance; rearrangement;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedlwp:97969. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anna Oates (email available below). General contact details of provider: https://edirc.repec.org/data/frbslus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.