• Graduate Program
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Summer School
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Foundations of Machine Learning with Applications in Python
      • Marketing Research with Purpose
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Tinbergen Institute Summer School Program
    • Annual Tinbergen Institute Conference archive
  • News
  • Summer School
    • Deep Learning
    • Economics of Blockchain and Digital Currencies
    • Foundations of Machine Learning with Applications in Python
    • Marketing Research with Purpose
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
  • Alumni

Opschoor, A. and Lucas, A. (2023). Time-varying variance and skewness in realized volatility measures International Journal of Forecasting, 39(2):827--840.


  • Journal
    International Journal of Forecasting

We propose new empirical models to capture the dynamics of the variance and skewness in realized volatility measures. We find that time-variation in variance and skewness of realized measures is a key empirical feature, even after accounting for well-known, stylized facts such as long-memory-type persistence and large incidental observations. Using a broad range of 89 US stocks across different sectors over 2001–2019, we show that these are not incidental phenomena of a few stocks but are widely shared. Accounting for dynamics in the variance and skewness of realized measures results in significantly better in-sample fit and out-of-sample unconditional density and quantile forecasts.