After VaR: The Theory, Estimation, and Insurance Applications of Quantile-Based Risk Measures
By Kevin Dowd, David P. Blake. 2006
222 citations recorded by the source corpus.
We discuss a number of quantile-based risk measures (QBRMs) that have recently been developed in the financial risk and actuarial/insurance literatures. The measures considered include the Value-at-Risk (VaR), coherent risk measures, spectral risk measures, and distortion risk measures. We discuss and compare the properties of these different measures, and point out that the VaR is seriously flawed. We then discuss how QBRMs can be estimated, and discuss some of the many ways they might be applied to insurance risk problems. These applications are typically very complex, and this complexity means that the most appropriate estimation method will often be some form of stochastic simulation.
Subfields: Demography, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ba9d6645047044bdb7cae169742cc1f51b906946
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Citations in the corpus
- Risk Measurement Performance of Alternative Distribution Functions (2008, background): “Dowd and Blake (2006) provide an extensive discussion on the use of VaR measures in the insurance sector.”
- Frontiers in VaR forecasting and backtesting (2016, background): “…recent Solvency 2 regulation also establishes it as the risk measure to be considered by insurance companies operating in the European Union; see Dowd and Blake (2006) and Sandström (2011) for a description of applications of VaR in the insurance ∗School of Business and Economics, Universidad…”