• Graduate program
    • Why Tinbergen Institute?
    • Research Master
    • Admissions
    • Course Registration
    • Facilities
    • PhD Vacancies
    • Selected PhD Placements
    • Research Master Business Data Science
  • Research
  • Browse our Courses
  • Summer School
  • Events
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Gender in Society
      • Machine Learning for Business
      • Marketing Research with Purpose
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • 16th Tinbergen Institute Annual Conference
    • Annual Tinbergen Institute Conference
  • News
  • Alumni

Creal, D., Koopman, S. and Lucas, A. (2011). A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations Journal of Business and Economic Statistics, 29(4):552--563.


  • Journal
    Journal of Business and Economic Statistics

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations and volatilities. When we account for heavy tails of distributions, we obtain estimates that are more robust to large innovations. We provide an empirical illustration for a panel of daily equity returns. {\textcopyright} 2011 American Statistical Association.