Semimartingale Models

Semimartingale Models is a mathematical framework in stochastic process theory characterized by its decomposition into a local martingale and a finite-variation process, specifically designed for modeling high-frequency time series data exhibiting both continuous and discontinuous sample paths.

542

Publications

28.6K

Citations

599

Authors

375

Institutions

Publications per year

2017–2026

46

Authors

599

  1. FR
    Francesco Russo

    École Nationale Supérieure de Techniques Avancées, France

    14 publications · 526 citations

    13

    h-index

  2. JJ
    Jean Jacod

    Sorbonne Université, France

    13 publications · 2K citations

    12

    h-index

  3. PP
    Philip Protter

    Cornell University, United States

    11 publications · 997 citations

    11

    h-index

  4. MS
    Martin Schweizer

    ETH Zurich, Switzerland

    11 publications · 881 citations

    10

    h-index

  5. MP
    Mark Podolskij

    Aarhus University, Denmark

    9 publications · 556 citations

    9

    h-index

1–5 of 599

Venues

  1. The Annals of Probability

    37 publications · 3.1K citations

    28

    h-index

  2. The Annals of Applied Probability

    30 publications · 2.3K citations

    21

    h-index

  3. Stochastic Processes and their Applications

    39 publications · 1.9K citations

    21

    h-index

  4. SSRN Electronic Journal

    Repository · 28 publications · 727 citations

    17

    h-index

  5. Finance and Stochastics

    17 publications · 1.5K citations

    16

    h-index

1–5