Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2021

Abstract

We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. The new methods are applied in a study on the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement.

Keywords

high-frequency data, jumps, robust regression, semimartingale, news announcements, news surprises, investor disagreement, volume, volatility

Discipline

Economics

Research Areas

Applied Microeconomics

Publication

Journal of Business and Economic Statistics

Volume

39

Issue

4

First Page

1015

Last Page

1025

ISSN

0735-0015

Identifier

10.1080/07350015.2020.1753526

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

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Included in

Economics Commons

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