English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this contribution we consider the overall risk given as the sum of randomsubrisks $\mathbf{X}_j$ in the context of value-at-risk (VaR) based riskcalculations. If we assume that the undertaking knows the parametricdistribution family subrisk $\mathbf{X}_j=\mathbf{X}_j(\theta_j)$, but does notknow the true parameter vectors $\theta_j$, the undertaking faces parameteruncertainty. To assess the appropriateness of methods to model parameteruncertainty for risk capital calculation we consider a criterion introduced inthe recent literature. According to this criterion, we demonstrate that, ingeneral, appropriateness of a risk capital model for each subrisk does notimply appropriateness of the model on the aggregate level of the overallrisk.\\ For the case where the overall risk is given by the sum of normallydistributed subrisks we prove a theoretical result leading to an appropriateintegrated risk capital model taking parameter uncertainty into account. Basedon the theorem we develop a method improving the approximation of the requiredconfidence level simultaneously for both - on the level of each subrisk as wellas for the overall risk.

Parameter uncertainty for integrated risk capital calculations based on normally distributed subrisks