An econometric analysis of volatility discovery

Gustavo Fruet Dias, Fotis Papailias, Cristina Scherrer

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Abstract

We investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component.
The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis.
Original languageEnglish
JournalJournal of Business & Economic Statistics
Early online date15 Dec 2023
DOIs
Publication statusE-pub ahead of print - 15 Dec 2023

Keywords

  • long memory
  • fractionally cointegrated vector autoregressive model
  • realized measures
  • market microstructure
  • price discovery
  • high-frequency data
  • double asymptotics
  • High-frequency data
  • Long memory
  • Price discovery
  • Market microstructure
  • Fractionally cointegrated vector autoregressive model
  • Realized measures
  • Double asymptotics

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