Supervisory Data Quality

Supervisory Data Quality is a formal assessment framework that specifies the accuracy, completeness, consistency, timeliness, and reliability of financial reporting data submitted by institutions to regulatory supervisors to ensure its fitness for prudential oversight and policy decision-making.

9

Publications

215

Citations

25

Authors

16

Institutions

Publications per year

2017–2026

3

Authors

25

  1. MM
    Martin Morris

    McGill University, Canada

    1 publications · 26 citations

    1

    h-index

  2. JP
    Joe Peek

    Federal Reserve Bank of Boston, United States

    1 publications · 12 citations

    1

    h-index

  3. UB
    Urs Birchler

    Swiss National Bank, Switzerland

    1 publications · 22 citations

    1

    h-index

  4. YZ
    Yukun Zhu

    Qingdao University, China

    1 publications · 58 citations

    1

    h-index

  5. AT
    Angelos T. Vouldis

    European Central Bank, Germany

    1 publications · 35 citations

    1

    h-index

1–5 of 25

Venues

  1. Applied Economics

    1 publications · 22 citations

    1

    h-index

  2. Behavioural and Cognitive Psychotherapy

    1 publications · 26 citations

    1

    h-index

  3. 1

    h-index

  4. SSRN Electronic Journal

    Repository · 1 publications · 35 citations

    1

    h-index

  5. New England economic review

    1 publications · 12 citations

    1

    h-index

1–5