Statistical inferences for price staleness

Aleksey Kolokolov, Giulia Livieri and Davide Pirino

No 236, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

Abstract: Asset transaction prices sampled at high frequency are much staler than one might expect in the sense that they frequently lack new updates showing zero returns. In this paper, we propose a theoretical framework for formalizing this phenomenon. It hinges on the existence of a latent continuous-time stochastic process pt valued in the open interval (0; 1), which represents at any point in time the probability of the occurrence of a zero return. Using a standard infill asymptotic design, we develop an inferential theory for nonparametrically testing, the null hypothesis that pt is constant over one day. Under the alternative, which encompasses a semimartingale model for pt, we develop non-parametric inferential theory for the probability of staleness that includes the estimation of various integrated functionals of pt and its quadratic variation. Using a large dataset of stocks, we provide empirical evidence that the null of the constant probability of staleness is fairly rejected. We then show that the variability of pt is mainly driven by transaction volume and is almost unaffected by bid-ask spread and realized volatility.

Keywords: staleness; idle time; liquidity; zero returns; stable convergence (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm and nep-mst
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:236

DOI: 10.2139/ssrn.3283628

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