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Volatility Discovery

Author

Listed:
  • Gustavo Fruet Dias

    (Aarhus University and CREATES)

  • Cristina M. Scherrer

    (Aarhus University and CREATES)

  • Fotis Papailias

    (Queen's University Management School)

Abstract

TWe propose a novel way to assess information processing in a complex environment of market fragmentation. We take a different angle from the price discovery literature, and investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We show that our volatility discovery framework successfully identifies the leading market in the volatility process, whereas price discovery measures are unable to capture the dynamics of the market-specific volatilities. We compute volatility discovery for 30 stocks and find significant differences in how exchanges impound information into the effcient volatility, as ARCA and NYSE are more important than NASDAQ. Interestingly, price discovery measures suggest different results for nearly half the sample.

Suggested Citation

  • Gustavo Fruet Dias & Cristina M. Scherrer & Fotis Papailias, 2016. "Volatility Discovery," CREATES Research Papers 2016-07, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2016-07
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    References listed on IDEAS

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    Cited by:

    1. Carol Alexander & Daniel Heck & Andreas Kaeck, 2021. "The Role of Binance in Bitcoin Volatility Transmission," Papers 2107.00298, arXiv.org, revised Aug 2021.
    2. Rainer Baule & Bart Frijns & Milena E. Tieves, 2018. "Volatility discovery and volatility quoting on markets for options and warrants," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(7), pages 758-774, July.
    3. Gustavo F. Dias & Marcelo Fernandes & Cristina M. Scherrer, 2021. "Price Discovery in a Continuous-Time Setting [Price Discovery and Common Factor Models]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 985-1008.

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    More about this item

    Keywords

    fragmented markets; information processing; volatility persistency; market microstructure; price discovery; high-frequency data;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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