Implied Volatility Forecasting

Implied Volatility Forecasting is a quantitative methodology in financial econometrics that specifically models and predicts future market volatility by extracting forward-looking expectations from observed options prices, distinguished from historical volatility approaches by its reliance on market-implied derivatives data.

300

Publications

21.6K

Citations

548

Authors

393

Institutions

Publications per year

2017–2026

74

Authors

548

  1. SJ
    Stephen J. Taylor

    Lancaster University, United Kingdom

    8 publications · 1.6K citations

    8

    h-index

  2. MG
    Massimo Guidolin

    Bocconi University, Italy

    6 publications · 276 citations

    6

    h-index

  3. GS
    George Skiadopoulos

    University of Piraeus, Greece

    6 publications · 418 citations

    6

    h-index

  4. BJ
    Bent Jesper Christensen

    Aarhus University, Denmark

    5 publications · 1.6K citations

    5

    h-index

  5. SD
    Stavros Degiannakis

    Panteion University, Greece

    5 publications · 119 citations

    5

    h-index

1–5 of 548

Venues

  1. SSRN Electronic Journal

    Repository · 45 publications · 1.2K citations

    21

    h-index

  2. Journal of Futures Markets

    25 publications · 1.1K citations

    17

    h-index

  3. Journal of Banking & Finance

    13 publications · 1.9K citations

    13

    h-index

  4. Journal of Forecasting

    8 publications · 443 citations

    8

    h-index

  5. The Journal of Derivatives

    7 publications · 502 citations

    7

    h-index

1–5