Computational aspects of portfolio risk estimation in volatile markets: A survey

Frank J. Fabozzi, Stoyan V. Stoyanov, Svetlozar T. Rachev

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


Portfolio risk estimation requires appropriate modeling of fat-tails and asymmetries in dependence in combination with a true downside risk measure. In this survey, we discuss computational aspects of a Monte Carlo based framework for risk estimation and risk capital allocation. We review different probabilistic approaches focusing on practical aspects of statistical estimation and scenario generation. We discuss value-at-risk and conditional value-at-risk and comment on the implications of using a fat-tailed Monte Carlo framework for the reliability of risk estimates including model risk and Monte Carlo variability.

Original languageEnglish
Pages (from-to)103-120
Number of pages18
JournalStudies in Nonlinear Dynamics and Econometrics
Issue number1
StatePublished - Feb 2013


  • Conditional value at risk
  • Copula
  • Fat-tailed models
  • Monte Carlo
  • Value at risk


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