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Computational aspects of portfolio risk estimation in volatile markets: a survey
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Frank J. Fabozzi
,
Stoyan V. Stoyanov
Veröffentlicht/Copyright:
14. Februar 2013
Abstract
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.
Published Online: 2013-02-14
©2013 by Walter de Gruyter Berlin Boston
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Artikel in diesem Heft
- Forecast uncertainty and the Bank of England’s interest rate decisions
- A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series
- Learning under signal-to-noise ratio uncertainty
- Using transfer entropy to measure information flows between financial markets
- Computational aspects of portfolio risk estimation in volatile markets: a survey
Schlagwörter für diesen Artikel
conditional value at risk;
value at risk;
copula;
fat-tailed models;
Monte Carlo
Artikel in diesem Heft
- Forecast uncertainty and the Bank of England’s interest rate decisions
- A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series
- Learning under signal-to-noise ratio uncertainty
- Using transfer entropy to measure information flows between financial markets
- Computational aspects of portfolio risk estimation in volatile markets: a survey
