A semi-parametric approach to risk management

By N. H. Bingham, Rüdiger Kiesel, Rafael Schmidt. 2003

52 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a79d39f8203a35354a46b3ca657ac0870f89a4f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Rafael Schmidt
2. Qihe Tang
3. Gabriel Frahm
4. Rob Kaas
5. Marc J. Goovaerts
6. Roger J. A. Laeven
7. Markus Junker
8. David J. Aldous

In Finance

1. Omiros Papaspiliopoulos
2. Rafael Schmidt
3. Gareth O. Roberts
4. Paul Fearnhead
5. Rob Kaas
6. Marc J. Goovaerts
7. Gabriel Frahm
8. Roger J. A. Laeven

Citations in the corpus

• Comparison of semimartingales and Lévy processes (2007, background): “Further mixing-type representations of particular interest for financial mathematical models hold for multivariate t-distributions and elliptically contoured distributions (see [4]).”
• Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) (2006, background): “Methods are available to counter this which reduce the effective dimensionality from the number or assets to the number of industrial sectors; see for example Bingham et al. (2003).”
• Non‐parametric Estimation of Tail Dependence (2006, background): “…which are generated from a bivariate standard t-distribution with =1.5, 2, 3 degrees of freedom, i.e. a spherically contoured t-distribution with density generator g(u)= c(1+u/ )−(1+ /2) [for a detailed discussion of the latter and various applications to finance, see Bingham et al. (2003)].”
• The Tail Probability of Discounted Sums of Pareto-like Losses in Insurance (2005, methodology): “Following the work of Bingham et al. [6], we shall circumvent the limitations of the Black-ScholesMerton framework by assuming that vector (1.6) either has a multivariate normal variance-mean mixture with some mixing law, or follows a multivariate elliptical distribution.”
• Probability and Mathematical Genetics: More uses of exchangeability: representations of complex random structures (2009, background): “stantially simplified due to a personal communication from Kingman. Constructions of partially exchangeable arrays appear in Bayesian statistical modeling; see e.g. the family of copulae introduced in [19] in the context of a semi-parametric model for Value at Risk. 2.4 Fast forward Such partially ex”
• Estimating the tail-dependence coefficient: Properties and pitfalls (2005, methodology): “Because of its popularity in theory and practice, as illustrated, e.g., by Bingham et al. (2003) and Embrechts et al. (2003), we consider the class of elliptically contoured distribution, viz.”