Co-jump Detection

Co-Jump Detection is a statistical method in financial econometrics that identifies simultaneous discontinuous price movements across multiple assets within high-frequency financial data, distinguishing itself from univariate jump detection by explicitly modeling cross-asset dependency structures to capture contemporaneous jumps induced by common market shocks.

23

Publications

1.6K

Citations

62

Authors

51

Institutions

Publications per year

2017–2026

10

Authors

62

  1. JL
    Jérôme Lahaye

    Federal Reserve Bank of St. Louis, United States

    2 publications · 325 citations

    2

    h-index

  2. MB
    Markus Bibinger

    Humboldt-Universität zu Berlin, Germany

    2 publications · 86 citations

    2

    h-index

  3. LW
    Lars Winkelmann

    Freie Universität Berlin, Germany

    2 publications · 86 citations

    2

    h-index

  4. CJ
    Christopher J. Neely

    Federal Reserve Bank of St. Louis, United States

    2 publications · 325 citations

    2

    h-index

  5. SL
    Sébastien Laurent

    UCLouvain, Belgium

    2 publications · 325 citations

    2

    h-index

1–5 of 62

Venues

  1. Journal of Econometrics

    2 publications · 350 citations

    2

    h-index

  2. 2

    h-index

  3. Finance research letters

    2 publications · 75 citations

    2

    h-index

  4. SSRN Electronic Journal

    Repository · 1 publications · 27 citations

    1

    h-index

  5. Journal of Applied Econometrics

    1 publications · 298 citations

    1

    h-index

1–5