Jump Detection

Jump Detection is a methodological framework within high-frequency time series analysis that identifies abrupt, discontinuous changes in the sample path of a stochastic process, distinguishing itself from continuous variation by isolating non-continuous, short-duration deviations attributable to exogenous shocks or market microstructure effects.

279

Publications

20K

Citations

532

Authors

375

Institutions

Publications per year

2017–2026

57

Authors

532

  1. PQ
    Peihua Qiu

    University of Florida, United States

    12 publications · 333 citations

    11

    h-index

  2. JJ
    Jean Jacod

    Sorbonne Université, France

    11 publications · 1.8K citations

    11

    h-index

  3. YA
    Yacine Aı̈t-Sahalia

    Princeton University, United States

    8 publications · 1.4K citations

    8

    h-index

  4. GT
    George Tauchen

    Duke University, United States

    8 publications · 896 citations

    8

    h-index

  5. SL
    Sébastien Laurent

    UCLouvain, Belgium

    8 publications · 672 citations

    8

    h-index

1–5 of 532

Venues

  1. SSRN Electronic Journal

    Repository · 23 publications · 1.1K citations

    16

    h-index

  2. Journal of Econometrics

    13 publications · 1.1K citations

    12

    h-index

  3. The Annals of Statistics

    7 publications · 1.1K citations

    7

    h-index

  4. RePEc: Research Papers in Economics

    Repository · 6 publications · 158 citations

    6

    h-index

  5. Technometrics

    6 publications · 203 citations

    6

    h-index

1–5