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Empirical Evidence on Jumps and Large Fluctuations in Individual Stocks


  • Diep Duong

    () (Rutgers University)

  • Norman R. Swanson

    () (Rutgers University)


We make use of the extant testing methodology of Barndorff-Nielsen and Shephard (2006) and Aït-Sahalia and Jacod (2009a,b,c) to examine the importance of jumps, and in particular “large" and “small" jumps, using high frequency price returns on 25 stocks in the DOW 30 and S&P futures index. In particular, we examine jumps from both the perspective of their contribution to overall realized variation and their contribution to predictive regressions of realized volatility. We find evidence of jumps in around 22.8% of the days during the 1993-2000 period, and in 9.4% of the days during the 2001-2008 period, which implies more (jump induced) turbulence in financial markets in the previous decade than the current decade. Also, it appears that frequent “small" jumps of the 1990s have been replaced to some extent with relatively infrequent "large" jumps in recent years. Interestingly, this result holds for all of the stocks that we examine, supporting the notion that there is strong comovement across jump components for a wide variety of stocks, as discussed in Bollerslev, Law and Tauchen (2008). In our prediction experiments using the class of linear and nonlinear HAR-RV, HAR-RV-J and HAR-RV-CJ models proposed by Müller, Dacorogna, Davé, Olsen, Puctet, and von Weizsäckeret (1997), Corsi (2004) and Andersen, Bollerslev and Diebold (2007). we find that the “linear" model performs well for only very few stocks, while there is significant improvement when instead using the “square root" model. Interestingly, the “log" model, which performs very well in their study of market indices, performs approximately equally as well as the square root model when our longer sample of market index data is used. Moreover, the log model, while yielding marked predictability improvements for individual stocks, can actually only be implemented for 7 of our 25 stocks, due to data singularity issues associated with the incidence of jumps at the level of individual stocks.

Suggested Citation

  • Diep Duong & Norman R. Swanson, 2011. "Empirical Evidence on Jumps and Large Fluctuations in Individual Stocks," Departmental Working Papers 201116, Rutgers University, Department of Economics.
  • Handle: RePEc:rut:rutres:201116

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    References listed on IDEAS

    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    2. Ole Barndorff-Nielsen & Svend Erik Graversen & Jean Jacod & Mark Podolskij & Neil Shephard, 2004. "A Central Limit Theorem for Realised Power and Bipower Variations of Continuous Semimartingales," Economics Papers 2004-W29, Economics Group, Nuffield College, University of Oxford.
    3. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 456-499.
    4. Peter Carr & Liuren Wu, 2003. "What Type of Process Underlies Options? A Simple Robust Test," Journal of Finance, American Finance Association, vol. 58(6), pages 2581-2610, December.
    5. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    More about this item


    Itô semi-martingale; realized volatility ; jumps; multipower variation; tripower variation; truncated power variation; quarticity; infinite activity jumps;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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