Bayesian Stochastic Volatility Models

Bayesian Stochastic Volatility Models is a class of latent variable models in econometrics that incorporates Bayesian inference to estimate time-varying volatility through a stochastic process, distinguishing itself by explicitly modeling the volatility as a latent state governed by a random dynamic evolution and using prior distributions to parameterize uncertainty in the volatility process.

118

Publications

4.7K

Citations

187

Authors

171

Institutions

Publications per year

2017–2026

41

Authors

187

  1. JC
    Joshua C. C. Chan

    Australian National University, Australia

    6 publications · 191 citations

    7

    h-index

  2. MM
    Massimiliano Marcellino

    Bocconi University, Italy

    4 publications · 487 citations

    5

    h-index

  3. TE
    Todd E. Clark

    Federal Reserve Bank of Cleveland, United States

    4 publications · 487 citations

    4

    h-index

  4. MK
    Mike K. P. So

    Hong Kong University of Science and Technology, Hong Kong SAR China

    4 publications · 169 citations

    4

    h-index

  5. RK
    Robert Kohn

    UNSW Sydney, Australia

    4 publications · 114 citations

    4

    h-index

1–5 of 187

Venues

  1. SSRN Electronic Journal

    Repository · 10 publications · 289 citations

    10

    h-index

  2. Journal of Business and Economic Statistics

    JBES · 8 publications · 366 citations

    8

    h-index

  3. Econometric Reviews

    8 publications · 260 citations

    8

    h-index

  4. Journal of Econometrics

    5 publications · 1K citations

    6

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 5 publications · 250 citations

    5

    h-index

1–5