Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2017

Abstract

We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals. In an empirical application, we use the developed inference techniques to test the temporal stability of market jump betas.

Keywords

efficient estimation, high-frequency data, jumps, LAMN, regression, semimartingale, specification test, stochastic volatility.

Discipline

Econometrics | Economic Theory

Research Areas

Econometrics

Publication

Econometrica

Volume

85

Issue

1

First Page

173

Last Page

195

ISSN

0012-9682

Identifier

10.3982/ECTA12962

Publisher

Econometric Society

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.3982/ECTA12962

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