High-frequency Data Cleaning

High-Frequency Data Cleaning is a systematic methodological framework within financial econometrics that distinguishes itself by addressing the unique noise, irregularities, and structural limitations inherent in high-frequency financial time series data through specialized preprocessing techniques such as outlier filtration, tick-level synchronization, and microstructure noise reduction.

16

Publications

839

Citations

99

Authors

45

Institutions

Publications per year

2017–2026

10

Authors

99

  1. IH
    Ib Hansen

    Bank of Italy, Italy

    2 publications · 157 citations

    2

    h-index

  2. HL
    Hwayun Lee

    Central Bank of Brazil, Brazil

    2 publications · 157 citations

    2

    h-index

  3. ÁB
    Ádám Banai

    Hungarian National Bank, Hungary

    2 publications · 157 citations

    2

    h-index

  4. GH
    Grzegorz Hałaj

    European Central Bank, Germany

    2 publications · 157 citations

    2

    h-index

  5. SF
    Soeren Friedrich

    Federal Reserve Bank of New York, United States

    2 publications · 157 citations

    2

    h-index

1–5 of 99

Venues

  1. SSRN Electronic Journal

    Repository · 3 publications · 292 citations

    3

    h-index

  2. Flow Measurement and Instrumentation

    1 publications · 24 citations

    1

    h-index

  3. The Astrophysical Journal

    ApJ · 1 publications · 56 citations

    1

    h-index

  4. 1

    h-index

  5. Tellus B

    1 publications · 33 citations

    1

    h-index

1–5