Show simple item record

Jump Regressions

dc.contributor.author Li, J
dc.contributor.author Tauchen, George E
dc.contributor.author Todorov, V
dc.date.accessioned 2016-12-19T19:42:39Z
dc.date.issued 2017-01-01
dc.identifier.issn 0012-9682
dc.identifier.uri https://hdl.handle.net/10161/13285
dc.description.abstract © 2017 The Econometric SocietyWe 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.
dc.relation.ispartof Econometrica
dc.relation.isversionof 10.3982/ECTA12962
dc.title Jump Regressions
dc.type Journal article
pubs.begin-page 173
pubs.end-page 195
pubs.issue 1
pubs.organisational-group Duke
pubs.organisational-group Economics
pubs.organisational-group Trinity College of Arts & Sciences
pubs.publication-status Published
pubs.volume 85
dc.identifier.eissn 1468-0262


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record