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Mixed-scale jump regressions with bootstrap inference

Publication ,  Journal Article
Li, J; Todorov, V; Tauchen, G; Chen, R
Published in: Journal of Econometrics
December 1, 2017

We develop an efficient mixed-scale estimator for jump regressions using high-frequency asset returns. A fine time scale is used to accurately identify the locations of large rare jumps in the explanatory variables such as the price of the market portfolio. A coarse scale is then used in the estimation in order to attenuate the effect of trading frictions in the dependent variable such as the prices of potentially less liquid assets. The proposed estimator has a non-standard asymptotic distribution that cannot be made asymptotically pivotal via studentization. We propose a novel bootstrap procedure for feasible inference and justify its asymptotic validity. We show that the bootstrap provides an automatic higher-order asymptotic approximation by accounting for the sampling variation in estimates of nuisance quantities that are used in efficient estimation. The Monte Carlo analysis indicates good finite-sample performance of the general specification test and confidence intervals based on the bootstrap. We apply the method to a high-frequency panel of Dow stock prices together with the market index defined by the S&P 500 index futures over the period 2007–2014. We document remarkable temporal stability in the way that stocks react to market jumps. However, this relationship for many of the stocks in the sample is significantly noisier and more unstable during sector-specific jump events.

Duke Scholars

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Published In

Journal of Econometrics

DOI

EISSN

1872-6895

ISSN

0304-4076

Publication Date

December 1, 2017

Volume

201

Issue

2

Start / End Page

417 / 432

Related Subject Headings

  • Econometrics
  • 4905 Statistics
  • 3802 Econometrics
  • 3801 Applied economics
  • 1403 Econometrics
  • 1402 Applied Economics
  • 0104 Statistics
 

Citation

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Li, J., Todorov, V., Tauchen, G., & Chen, R. (2017). Mixed-scale jump regressions with bootstrap inference. Journal of Econometrics, 201(2), 417–432. https://doi.org/10.1016/j.jeconom.2017.08.017
Li, J., V. Todorov, G. Tauchen, and R. Chen. “Mixed-scale jump regressions with bootstrap inference.” Journal of Econometrics 201, no. 2 (December 1, 2017): 417–32. https://doi.org/10.1016/j.jeconom.2017.08.017.
Li J, Todorov V, Tauchen G, Chen R. Mixed-scale jump regressions with bootstrap inference. Journal of Econometrics. 2017 Dec 1;201(2):417–32.
Li, J., et al. “Mixed-scale jump regressions with bootstrap inference.” Journal of Econometrics, vol. 201, no. 2, Dec. 2017, pp. 417–32. Scopus, doi:10.1016/j.jeconom.2017.08.017.
Li J, Todorov V, Tauchen G, Chen R. Mixed-scale jump regressions with bootstrap inference. Journal of Econometrics. 2017 Dec 1;201(2):417–432.
Journal cover image

Published In

Journal of Econometrics

DOI

EISSN

1872-6895

ISSN

0304-4076

Publication Date

December 1, 2017

Volume

201

Issue

2

Start / End Page

417 / 432

Related Subject Headings

  • Econometrics
  • 4905 Statistics
  • 3802 Econometrics
  • 3801 Applied economics
  • 1403 Econometrics
  • 1402 Applied Economics
  • 0104 Statistics