Volatility Forecasts and Value at Risk Evaluation for the MSCI North America Index

  • Momtchil Pojarliev
  • Wolfgang Polasek
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This paper compares different models for volatility forecasts with respect to the value at risk performance (VaR). The VaR measures the potential loss of a portfolio for the next period at a given significance level. We focus on the question if the choice of the appropriate volatility forecasting model is important for the VaR estimation. We compare the forecasting performance of several volatility models for the returns of the MSCI North America index. The resulting VaR estimators are evaluated by comparing the empirical failure rate with the forecasting performance.


Forecast Performance GARCH Model Volatility Forecast Naive Model Auxiliary Regression 
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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Momtchil Pojarliev
    • 1
  • Wolfgang Polasek
    • 2
  1. 1.INVESCO Asset ManagementFrankfurtGermany
  2. 2.Institute of Advanced Studies, ViennaWien

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