An econometric analysis of volatility discovery

Gustavo Fruet Dias, Fotis Papailias and Cristina Scherrer

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: double asymptotics; fractionally cointegrated vector autoregressive model; high-frequency data; long memory; market microstructure; price discovery; realized measures (search for similar items in EconPapers)
JEL-codes: C30 (search for similar items in EconPapers)
Date: 2023-12-15
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Published in Journal of Business and Economic Statistics, 15, December, 2023. ISSN: 0735-0015

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