Abstract
Distortion functions are employed to define measures of risk. Receiver operating characteristic (ROC) curves are used to describe the performance of parametrized test families in testing a simple null hypothesis against a simple alternative. This paper provides a connection between distortion functions on the one hand, and ROC curves on the other. This leads to a new interpretation of some well-known classes of distortion risk measures, and to a new notion of divergence between probability measures.
Acknowledgements
The author gratefully acknowledges comments by two anonymous reviewers, which helped improve the paper.
References
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© 2017 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Portfolio optimization under dynamic risk constraints: Continuous vs. discrete time trading
- On risk measuring in the variance-gamma model
- Distortion risk measures, ROC curves, and distortion divergence
- EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies
- Optimal expected utility risk measures
Articles in the same Issue
- Frontmatter
- Portfolio optimization under dynamic risk constraints: Continuous vs. discrete time trading
- On risk measuring in the variance-gamma model
- Distortion risk measures, ROC curves, and distortion divergence
- EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies
- Optimal expected utility risk measures