Extreme Value Theory: Potential And Limitations As An Integrated Risk Management Tool

By Paul Embrechts. 2000

65 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/be972add9174af19abd3e30e3fb7eb1f9832799d

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Citations in the corpus

Financial Risk and Heavy Tails (2003, methodology): “For example, in the case of a stationary ARCH(1) model, Xt = √ α0 + α1X2t−1Zt, with α0 > 0 and α1 ∈ (0, 2eγ), where γ is Euler’s constant24, Embrechts, Klüppelberg and Mikosch [EKM97] show that the unconditional distribution is formally heavy-tailed, that is P(X > x) ∼ cx−α, x →∞.”
The Generalized Extreme Value (GEV) Distribution, Implied Tail Index and Option Pricing (2011, unknown): “3 Embrechts (1999, 2000) considers the potential and limitations of extreme value theory for risk management.”
Expected Shortfall as a Tool for Financial Risk Management (2001, background): “In both cases, in the case of normal distributed returns of a portfolio, one can show that the “non–additivity” is actually a “sub–additivity”: total VaR is always less or equal than the sum of partial (by position or by risk driver) VaR’s.”
Expected Shortfall: a natural coherent alternative to Value at Risk (2001, background): “In this paper and henceforth we are going to take these axioms seriously as many other groups of researchers [4, 5, 6, 7, 9, 10], practitioners and regulators [11] have begun to do.”
Dynamic Copula Modelling for Value at Risk (2006, unknown): “In this case we have the so-called meta joint distribution functions, whose theoretical background has been studied recently by Embrechts, Hoing, and Juri (2003) and Fang and Fang (2002). In the following, we briefly describe the conditional marginal and copula functions used to construct multivaria”