An Econometric Analysis of Volatility Discovery

Gustavo Fruet Dias, Fotis Papailias and Cristina Scherrer
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Gustavo Fruet Dias: School of Economics, University of East Anglia
Fotis Papailias: Kings Business School, Kings College London
Cristina Scherrer: Department of Finance, London School of Economics

No 2024-01, University of East Anglia School of Economics Working Paper Series from School of Economics, University of East Anglia, Norwich, UK.

Abstract: We investigate information processing in the stochastic process driving stocks 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 inte-grated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and infill 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: long memory; fractionally cointegrated vector autoregressive model; realized measures; market microstructure; price discovery; high-frequency data; double asymptotics (search for similar items in EconPapers)
Date: 2024-01
New Economics Papers: this item is included in nep-mst and nep-rmg
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