Quantile information share

Abstract

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.

Robust information share measures with an application on the international crude oil markets

“…First, to the best of our knowledge, this is the first study to comprehensively investigate the robust information share measures. The proposed robust VECM and the associated HIS/MIS measure therefore significantly complement the existing research, such as Lien and Wang (2019). Second, our simulation study demonstrates the nonnegligible biasness of the ordinary information share measures in the presence of outliers, and more importantly, the effectiveness of the proposed robust measures to resolve this issue.…”

“…In particular, the estimated HIS's depend on the ordering of the price series—the HIS of a market is the largest when it is ordered as the first market and smallest when it is the last. Consequentially, the HIS of each market is identified only up to an interval, and one needs to compute the midpoint between the minimum and maximum values obtained from all possible permutations of orderings to derive the point estimate (Baillie et al, 2002; Hasbrouck, 2003; Lien & Wang, 2019; Ozturk et al, 2017). As a remedial approach, Hasbrouck (2019) proposes a method to lower the contemporaneous correlations of the price series using high‐frequency data, so that the gap of the minimum and the maximum values could be reduced.…”

Quantile information share under Markov regime‐switching

“…Neither the rolling window approach nor the time‐varying second‐moment model explains the source of the variation. Lien and Wang (2019) argue that the IS may change when the quantiles of the market prices change. The resulting measure is, therefore, called quantile information share (QIS).…”