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Quantile information share
Author(s) -
Lien Donald,
Wang Zijun
Publication year - 2019
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.21940
Subject(s) - quantile , quantile regression , futures contract , econometrics , equity (law) , economics , futures market , financial economics , index (typography) , stock index futures , share price , stock market index , stock market , computer science , stock exchange , finance , geography , world wide web , political science , law , context (archaeology) , archaeology
This paper presents a new method to estimate Hasbrouck‐type market information share in price discovery. The prevailing market information share is calculated on the basis of conditional mean. We propose a conditional quantile regression approach to obtain a new market information share measure, quantile information share, which varies across the combinations of different price quantiles. The method is illustrated with two data sets, one on the spot and futures markets in pricing S&P 500 equity index, and the other on price discovery for a cross‐listed stock.