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Bounds for Quantile-Based Risk Measures of Functions of Dependent Random Variables
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Marcelo Goncalves
Veröffentlicht/Copyright:
11. März 2010
Abstract
This paper introduces two techniques for computing bounds for several quantile-based risk measures based on distortion functions. Knowledge about the marginal distribution of the involved random variables is assumed with the optional assumption of some partial information about the structure of dependence. The aim is to derive bounds for risk measures of functions of dependent random variables. Several examples taken from an insurance context are given. We use Embrechts et al. (2003) methodology and the stochastic ordering approach to derive bounds for various risk measures in the bi-dimensional and the multidimensional cases.
Published Online: 2010-03-11
Published in Print: 2008-April
© Heldermann Verlag
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Artikel in diesem Heft
- One-sided and Two-sided Critical Values for Dixon's Outlier Test for Sample Sizes up to n = 30
- Exact Distributions of Products of Certain Random Variables
- Two Old Statistical Methods in a New Stochastic Outfit
- Optimal Maintenance/Replacement-Policy for Deteriorating Systems under Two-Phase Maintenance Strategy
- Bounds for Quantile-Based Risk Measures of Functions of Dependent Random Variables
- The Distribution of the Profile of Aluminium Coil
- A Note on the Efficiency of Nonparametric Control Chart for Monitoring Process Variability
- Control Charts for Particles in the Semiconductor Manufacturing Process
- Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM
- A Critical Note on the Guide to the Expression of Uncertainty in Measurement (GUM)
