Jump Regressions

Jia Li, Viktor Todorov, George Tauchen

Research output: Contribution to journalArticle

26 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)173-195
Number of pages23
JournalEconometrica
Volume85
Issue number1
DOIs
StatePublished - Jan 1 2017

Keywords

  • Efficient estimation
  • LAMN
  • high-frequency data
  • jumps
  • regression
  • semimartingale
  • specification test
  • stochastic volatility

ASJC Scopus subject areas

  • Economics and Econometrics

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  • Cite this

    Li, J., Todorov, V., & Tauchen, G. (2017). Jump Regressions. Econometrica, 85(1), 173-195. https://doi.org/10.3982/ECTA12962