• Graduate Programs
  • Research
  • Browse our Courses
  • Events
    • Events Calendar
    • Events Archive
    • Summer School
      • Applied Public Policy Evaluation
      • Deep Learning
      • Development Economics
      • Economics of Blockchain and Digital Currencies
      • Economics of Climate Change
      • The Economics of Crime
      • Foundations of Machine Learning with Applications in Python
      • From Preference to Choice: The Economic Theory of Decision-Making
      • Inequalities in Health and Healthcare
      • Marketing Research with Purpose
      • Markets with Frictions
      • Modern Toolbox for Spatial and Functional Data
      • Sustainable Finance
      • Tuition Fees and Payment
      • Business Data Science Summer School Program
    • Tinbergen Institute Lectures
    • 2026 Tinbergen Institute Opening Conference
    • Annual Tinbergen Institute Conference
  • News
  • Summer School
    • Applied Public Policy Evaluation
    • Deep Learning
    • Development Economics
    • Economics of Blockchain and Digital Currencies
    • Economics of Climate Change
    • The Economics of Crime
    • Foundations of Machine Learning with Applications in Python
    • From Preference to Choice: The Economic Theory of Decision-Making
    • Inequalities in Health and Healthcare
    • Marketing Research with Purpose
    • Markets with Frictions
    • Modern Toolbox for Spatial and Functional Data
    • Sustainable Finance
    • Tuition Fees and Payment
  • Alumni

Ardia, D. and Hoogerheide, L. (2014). GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts Economics Letters, 123(2):187--190.


  • Affiliated author
    Lennart Hoogerheide
  • Publication year
    2014
  • Journal
    Economics Letters

We analyze the impact of the estimation frequency-updating parameter estimates on a daily, weekly, monthly or quarterly basis-for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S\&P 500 index. We assess the implication for one-day ahead 95% and 99% Value-at-Risk (VaR) forecasts with the test for correct conditional coverage of Christoffersen (1998) and for Expected Shortfall (ES) forecasts with the block-bootstrap test of ES violations of Jalal and Rockinger (2008). Using the false discovery rate methodology of Storey (2002) to estimate the percentage of stocks for which the model yields correct VaR and ES forecasts, we conclude that there is no difference in performance between updating the parameter estimates of the GARCH equation at a daily or weekly frequency, whereas monthly or even quarterly updates are only marginally outperformed. {\textcopyright} 2014 Elsevier B.V.