 Variance of the CTE EstimatorB. John Manistre and Geoffrey Hancock North American Actuarial Journal, 2005, vol. 9, issue 2, 129-156 Abstract: The Conditional Tail Expectation (CTE), also called Expected Shortfall or Tail-VaR, is a robust, convenient, practical, and coherent measure for quantifying financial risk exposure. The CTE is quickly becoming the preferred measure for statutory balance sheet valuation whenever real-world stochastic methods are used to set liability provisions. We look at some statistical properties of the methods that are commonly used to estimate the CTE and develop a simple formula for the variance of the CTE estimator that is valid in the large sample limit. We also show that the formula works well for finite sample sizes. Formula results are compared with sample values from realworld Monte Carlo simulations for some common loss distributions, including equity-linked annuities with investment guarantees, whole life insurance and operational risks. We develop the CTE variance formula in the general case using a system of biased weights and explore importance sampling, a form of variance reduction, as a way to improve the quality of the estimators for a given sample size. The paper closes with a discussion of practical applications. Date: 2005 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:uaajxx:v:9:y:2005:i:2:p:129-156 Ordering information: This journal article can be ordered from DOI: 10.1080/10920277.2005.10596207 Access Statistics for this article North American Actuarial Journal is currently edited by Kathryn Baker More articles in North American Actuarial Journal from Taylor & Francis Journals |
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