PRMIA 8010 Exam - Topic 1 Question 48 Discussion

Actual exam question for PRMIA's 8010 exam

Question #: 48
Topic #: 1

[All 8010 Questions]

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?

AThe univariate Gaussian model fails to fit to the empirical distributions of risk factors, notably their fat tails and skewness.

BDetermining the covariance matrix becomes an extremely difficult task as the number of risk factors increases.

CIt cannot capture linear relationships between risk factors.

DA single covariance matrix is insufficient to describe the fine codependence structure among risk factors as non-linear dependencies or tail correlations are not captured.

Show Suggested Answer

Suggested Answer: C

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.

by Danica at May 23, 2024, 12:14 AM

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Roxane

6 days ago

I think B is more of a challenge than a limitation.

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Lyndia

12 days ago

A is definitely a limitation, fat tails are a big deal!

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Sueann

18 days ago

I’m a bit confused about C; I thought the model could capture linear relationships, but maybe I'm mixing it up with something else.

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Louis

23 days ago

I practiced a question similar to this, and I think D makes sense because a single covariance matrix can't capture all the complexities.

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Lenna

28 days ago

I’m not entirely sure, but I feel like B could be a limitation too, especially when dealing with many risk factors.

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Myra

1 month ago

I remember discussing how the univariate Gaussian model struggles with fat tails, so I think A might be a limitation.

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Tamie

1 month ago

I'm feeling pretty confident about this one. The univariate Gaussian model has some well-known limitations, like failing to capture fat tails and skewness, as well as the difficulty of determining the covariance matrix. I'll go with option D.

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Joana

1 month ago

Okay, I think I've got this. The key is to identify the limitation that is not captured by the univariate Gaussian model, which seems to be option C.

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Tran

1 month ago

Hmm, I'm a bit confused by the wording here. I'll need to re-read the question and options a few times to make sure I understand what they're asking.

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Johnna

1 month ago

This seems like a tricky question. I'll need to think carefully about the limitations of the univariate Gaussian model for VaR calculations.

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German

1 month ago

I'm a bit confused by the wording of the question. Let me re-read it carefully and think through each of the options to determine which one is the exception.

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Ernestine

1 month ago

No problem, I've done this kind of thing before. I'll just quickly scan the options, select the one that looks like it will create a new sheet with the right name, and move on to the next question.

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Felicitas

2 months ago

I'm a bit confused by this question. It mentions a Windows Vista-based computer with Office 2010, but the options seem to be more generic database management actions. I'm not sure if those would apply in this specific software environment. I'd probably try option A or D, but I'd want to double-check that first.

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Lavera

2 months ago

I'm pretty sure the Section Tag displays the sheet number, so I'll go with option A.

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Reed

1 year ago

Isn't the Gaussian model like trying to fit a square peg in a round hole? Of course it can't capture the real-world complexities. I vote for A.

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Remedios

1 year ago

Haha, I bet the exam writers were having a field day coming up with these options. But in all seriousness, I think D is the correct answer.

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Wilda

1 year ago

I think D is the best option too, it covers the limitations of the univariate Gaussian model well.

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Gilma

1 year ago

Yeah, D makes sense because it mentions non-linear dependencies and tail correlations.

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Dortha

1 year ago

I agree, D seems like the most accurate choice.

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Kris

1 year ago

I'm going with B. As the number of risk factors increases, determining the covariance matrix becomes a nightmare. Who has time for that?

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Izetta

1 year ago

Definitely, it's a real challenge as the number of risk factors grows.

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Oliva

1 year ago

I agree, determining the covariance matrix with more risk factors is a hassle.

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Rosann

1 year ago

Definitely, it can be a nightmare to handle all those calculations.

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Devon

1 year ago

I agree, determining the covariance matrix for a large number of risk factors is a real challenge.

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Daniel

1 year ago

D seems like the best answer to me. The covariance matrix alone is not enough to describe the complex codependence structure among risk factors.

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Amber

1 year ago

Yes, D is the most accurate choice. Non-linear dependencies and tail correlations are not captured by a single covariance matrix.

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Aileen

1 year ago

I agree, D is the correct answer. The covariance matrix is not sufficient to capture all the dependencies among risk factors.

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Ma

2 years ago

I think the correct answer is A. The Gaussian model fails to capture the empirical distribution of risk factors, which often exhibit fat tails and skewness.

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