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Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach

Author

Listed:
  • Sojung Kim
  • Stefan Weber
Abstract
Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.

Suggested Citation

  • Sojung Kim & Stefan Weber, 2020. "Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach," Papers 2009.03653, arXiv.org, revised Jan 2022.
    • Handle: RePEc:arx:papers:2009.03653
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    File URL: http://arxiv.org/pdf/2009.03653
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    References listed on IDEAS

    as
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    Cited by:

    1. Soren Bettels & Stefan Weber, 2024. "An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models," Papers 2408.02401, arXiv.org.

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