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J. Risk Financial Manag., Volume 9, Issue 1 (March 2016) – 2 articles

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Article
VaR and CVaR Implied in Option Prices
J. Risk Financial Manag. 2016, 9(1), 2; https://doi.org/10.3390/jrfm9010002 - 29 Feb 2016
Cited by 16 | Viewed by 3063
Abstract
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of [...] Read more.
VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of measure are modest at the short horizons typically used in applications. The computation of CVaR from option price is very convenient, because this measure is not elicitable, making direct comparisons of statistical inferences from market data problematic. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
Article
The Two Defaults Scenario for Stressing Credit Portfolio Loss Distributions
J. Risk Financial Manag. 2016, 9(1), 1; https://doi.org/10.3390/jrfm9010001 - 31 Dec 2015
Cited by 1 | Viewed by 2688
Abstract
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by [...] Read more.
The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event is extremely rare. We provide an analytical approach to the calculation of the conditional loss distribution for the CreditRisk + portfolio model with independent random loss given default distributions. The analytical solution for this case can be used to check the accuracy of an approximation to the conditional loss distribution whereby the unconditional model is run with stressed input probabilities of default (PDs). It turns out that this approximation is unbiased. Numerical examples, however, suggest that the approximation may be seriously inaccurate but that the inaccuracy leads to overestimation of tail losses and, hence, the approach errs on the conservative side. Full article
(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)
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