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NEW QUESTION: 1
You have deployed an enterprise-wide host/endpoint technology for all of the company corporate PCs. Management asks you to block a selected set of applications on all corporate PCs. Which technology is the best option?
A. antivirus/antispyware software
B. application whitelisting/blacklisting
C. network NGFW
D. host-based IDS
Answer: B

NEW QUESTION: 2
Welche der folgenden Aussagen ist die BESTE Beschreibung eines Business Case?
A. Ein tragbares Gerät zur sicheren Aufbewahrung und zum sicheren Transport wichtiger Dokumente
B. Die Bedingungen in einem IT-Outsourcing-Vertrag.
C. Eine Beschwerde des Unternehmens über einen fehlenden Servicelevel.
D. Ein Tool zur Entscheidungsunterstützung und Planung, das die wahrscheinlichen Folgen einer Geschäftsaktion projiziert
Answer: D

NEW QUESTION: 3
Which of the following is not a limitation of the univariate Gaussian model to capture the codependence structure between risk factros used for VaR calculations?
A. The univariate Gaussian model fails to fit to the empirical distributions of risk factors, notably their fat tails and skewness.
B. It cannot capture linear relationships between risk factors.
C. Determining the covariance matrix becomes an extremely difficult task as the number of risk factors increases.
D. A single covariance matrix is insufficient to describe the fine codependence structure among risk factors as non-linear dependencies or tail correlations are not captured.
Answer: B
Explanation:
Explanation
In the univariate Gaussian model, each risk factor is modeled separately independent of the others, and the dependence between the risk factors is captured by the covariance matrix (or its equivalent combination of the correlation matrix and the variance matrix). Risk factors could include interest rates of different tenors, different equity market levels etc.
While this is a simple enough model, it has a number of limitations.
First, it fails to fit to the empirical distributions of risk factors, notably their fat tails and skewness. Second, a single covariance matrix is insufficient to describe the fine codependence structure among risk factors as non-linear dependencies or tail correlations are not captured. Third, determining the covariance matrix becomes an extremely difficult task as the number of risk factors increases. The number of covariances increases by the square of the number of variables.
But an inability to capture linear relationships between the factors is not one of the limitations of the univariate Gaussian approach - in fact it is able to do that quite nicely with covariances.
A way to address these limitations is to consider joint distributions of the risk factors that capture the dynamic relationships between the risk factors, and that correlation is not a static number across an entire range of outcomes, but the risk factors can behave differently with each other at different intersection points.