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The Identification of Price Jumps

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  • Jan Hanousek
  • Evzen Kocenda
  • Jan Novotny

Abstract

We performed an extensive simulation study to compare the relative performance of many price-jump indicators with respect to false positive and false negative probabilities. We simulated twenty different time series specifications with different intraday noise volatility patterns and price-jump specifications. The double McNemar (1947) non-parametric test has been applied on constructed artificial time series to compare fourteen different price-jump indicators that are widely used in the literature. The results suggest large differences in terms of performance among the indicators, but we were able to identify the best-performing indicators. In the case of false positive probability, the best-performing price-jump indicator is based on thresholding with respect to centiles. In the case of false negative probability, the best indicator is based on bipower variation.

Suggested Citation

  • Jan Hanousek & Evzen Kocenda & Jan Novotny, 2011. "The Identification of Price Jumps," CERGE-EI Working Papers wp434, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    • Handle: RePEc:cer:papers:wp434
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    Cited by:

    1. Jan Hanousek & Evzen Kocenda & Jan Novotny, 2014. "Price jumps on European stock markets," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 14(1), pages 10-22, March.
    2. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    3. Lavička, H. & Lichard, T. & Novotný, J., 2016. "Sand in the wheels or wheels in the sand? Tobin taxes and market crashes," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 328-342.
    4. Josip Arneriæ & Mario Matkoviæ, 2019. "Challenges of integrated variance estimation in emerging stock markets," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(2), pages 713-739.
    5. Milan Fičura, 2019. "Profitability of Trading in the Direction of Asset Price Jumps - Analysis of Multiple Assets and Frequencies," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(4), pages 385-401.
    6. Jan Novotn?? & Jan Hanousek & Ev??en Ko??enda, 2013. "Price Jump Indicators: Stock Market Empirics During the Crisis," William Davidson Institute Working Papers Series wp1050, William Davidson Institute at the University of Michigan.
    7. Barbara Będowska-Sójka, 2021. "Is liquidity wasted? The zero-returns on the Warsaw Stock Exchange," Annals of Operations Research, Springer, vol. 297(1), pages 37-51, February.
    8. Füss, Roland & Grabellus, Markus & Mager, Ferdinand & Stein, Michael, 2018. "Something in the air: Information density, news surprises, and price jumps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 50-75.
    9. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    10. Jan Hanousek & Evžen Kočenda & Jan Novotný, 2016. "Shluková analýza skoků na kapitálových trzích [Cluster Analysis of Jumps on Capital Markets]," Politická ekonomie, Prague University of Economics and Business, vol. 2016(2), pages 127-144.
    11. Jan Hanousek & Jan Novotný, 2014. "Cenové skoky během finanční nejistoty: od intuice k regulační perspektivě [Price Jumps during Financial Crisis: From Intuition to Financial Regulation]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(1), pages 32-48.
    12. repec:prg:jnlpep:v:preprint:id:703:p:1-17 is not listed on IDEAS

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    More about this item

    Keywords

    price jumps; price-jump indicators; non-parametric testing; Monte Carlo simulations; financial econometrics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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