Bayesian Garch Models

Bayesian GARCH Models is a statistical modeling framework within econometrics that integrates Bayesian inference methods with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) structures to estimate time-varying volatility parameters through prior distributions and posterior updating.

130

Publications

10.5K

Citations

211

Authors

152

Institutions

Publications per year

2017–2026

40

Authors

211

  1. CW
    Cathy W. S. Chen

    Feng Chia University, Taiwan

    7 publications · 223 citations

    7

    h-index

  2. DA
    David Ardia

    Vrije Universiteit Brussel, Belgium

    6 publications · 540 citations

    6

    h-index

  3. LB
    Luc Bauwens

    UCLouvain, Belgium

    5 publications · 228 citations

    5

    h-index

  4. AZ
    Arnold Zellner

    University of Chicago, United States

    5 publications · 3.9K citations

    5

    h-index

  5. MM
    Michael McAleer

    Erasmus University Rotterdam, Netherlands

    4 publications · 74 citations

    4

    h-index

1–5 of 211

Venues

  1. Journal of Econometrics

    9 publications · 1.3K citations

    9

    h-index

  2. SSRN Electronic Journal

    Repository · 9 publications · 295 citations

    9

    h-index

  3. Journal of Business and Economic Statistics

    JBES · 4 publications · 353 citations

    4

    h-index

  4. Journal of the American Statistical Association

    4 publications · 1.8K citations

    4

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 3 publications · 108 citations

    3

    h-index

1–5