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Statistical Inferences for Bivariate Time-Varying Price Staleness.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Statistical Inferences for Bivariate Time-Varying Price Staleness./
Author:	Zhu, Haibin.
Description:	1 online resource (144 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Contained By:	Dissertations Abstracts International85-04A.
Subject:	Mathematics. -
Online resource:	click for full text (PQDT)
ISBN:	9798380412742

Statistical Inferences for Bivariate Time-Varying Price Staleness.
Zhu, Haibin.

Statistical Inferences for Bivariate Time-Varying Price Staleness. - 1 online resource (144 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

Thesis (Ph.D.)--University of Macau, 2023.

Includes bibliographical references

The price staleness is referred to as the extent of zero returns in the price dynamics, which reflects the liquidity of financial markets. The proportion of the zero returns could be high in the high-frequency data sets, as pointed out by [1].In the first part of this thesis, we focus on the statistical inference of price staleness. Considering the price staleness as a dynamic system, in Chapters 2 and 3, we first study the statistical inference of the idiosyncratic price staleness and common price staleness between two assets, where the common price staleness characterizes the probability of the presence of common zero returns in the high-frequency data. We propose consistent estimators for the idiosyncratic and common price staleness under the new framework and establish their distributional theory. Moreover, we develop a feasible nonparametric test for the simultaneous constancy of the individual and common price staleness. Our empirical evidence supports the existence and non-constancy of the common price staleness. While the common price staleness could be treated as a proxy for market illiquidity, the idiosyncratic price staleness could be an effective proxy for individual stock illiquidity. In the spirit of this idea, we construct intraday portfolios with constraints of time-varying idiosyncratic staleness and report the gain in portfolio liquidity.Secondly, Phillips and Yu (2007) [2], and Buccheri et al. (2020) [3] found that the presence of zero returns affects the limiting behavior of realized power variations and realized covariation of semi-martingale. To further extend their results, in Chapter 4, we consider the bivariate constant price staleness in the estimation of realized covariation, and establish a unified result for the limiting theory of the realized power variations of multivariate semi-martingales. Our result includes the existing studies as special cases. Moreover, we provide a new asymptotic theory for realized covariation by considering bivariate constant price staleness processes between assets. Our limiting theory of the realized covariation could be applied to explaining Epp's effect, proposed by [4], from the perspective of price staleness. Our empirical evidence shows that bias-corrected covariance estimation improves portfolio performance.Finally, we extend the results of covariance estimation to the case of time-varying price staleness. We first propose a consistent estimator of spot covariance and later derive an inferential theory for the proposed estimator. Then, using an infill asymptotic design, we derive limit theorems for the functionals of covariance. We assess the finite sample performance of the asymptotic theory by Monte Carlo simulations.In Chapter 6, we give some concluding remarks and briefly highlight possible future research.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380412742Subjects--Topical Terms:

527692
Mathematics.
Subjects--Index Terms:

Financial marketsIndex Terms--Genre/Form:

554714
Electronic books.

Statistical Inferences for Bivariate Time-Varying Price Staleness.
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520 $a The price staleness is referred to as the extent of zero returns in the price dynamics, which reflects the liquidity of financial markets. The proportion of the zero returns could be high in the high-frequency data sets, as pointed out by [1].In the first part of this thesis, we focus on the statistical inference of price staleness. Considering the price staleness as a dynamic system, in Chapters 2 and 3, we first study the statistical inference of the idiosyncratic price staleness and common price staleness between two assets, where the common price staleness characterizes the probability of the presence of common zero returns in the high-frequency data. We propose consistent estimators for the idiosyncratic and common price staleness under the new framework and establish their distributional theory. Moreover, we develop a feasible nonparametric test for the simultaneous constancy of the individual and common price staleness. Our empirical evidence supports the existence and non-constancy of the common price staleness. While the common price staleness could be treated as a proxy for market illiquidity, the idiosyncratic price staleness could be an effective proxy for individual stock illiquidity. In the spirit of this idea, we construct intraday portfolios with constraints of time-varying idiosyncratic staleness and report the gain in portfolio liquidity.Secondly, Phillips and Yu (2007) [2], and Buccheri et al. (2020) [3] found that the presence of zero returns affects the limiting behavior of realized power variations and realized covariation of semi-martingale. To further extend their results, in Chapter 4, we consider the bivariate constant price staleness in the estimation of realized covariation, and establish a unified result for the limiting theory of the realized power variations of multivariate semi-martingales. Our result includes the existing studies as special cases. Moreover, we provide a new asymptotic theory for realized covariation by considering bivariate constant price staleness processes between assets. Our limiting theory of the realized covariation could be applied to explaining Epp's effect, proposed by [4], from the perspective of price staleness. Our empirical evidence shows that bias-corrected covariance estimation improves portfolio performance.Finally, we extend the results of covariance estimation to the case of time-varying price staleness. We first propose a consistent estimator of spot covariance and later derive an inferential theory for the proposed estimator. Then, using an infill asymptotic design, we derive limit theorems for the functionals of covariance. We assess the finite sample performance of the asymptotic theory by Monte Carlo simulations.In Chapter 6, we give some concluding remarks and briefly highlight possible future research.
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