Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

CFA Institute CFA-Level-II Exam - Topic 1 Question 72 Discussion

Actual exam question for CFA Institute's CFA-Level-II exam
Question #: 72
Topic #: 1
[All CFA-Level-II Questions]

Ernie Smith and Jama! Sims are analysts with the firm of Madison Consultants. Madison provides statistical modeling and advice to portfolio managers throughout the United States and Canada.

In an effort to estimate future cash flows and value the Canadian stock market. Smith has been examining* the country's aggregate retail sales. He runs two autoregressive regression models in an attempt to determine whether there are any patterns in the data, utilizing nine years of unadjusted monthly retail sales data. One model uses a lag one variable and the other adds a lag twelve variable. The results of both regressions are shown in Exhibits 1 and 2.

Sims has been assigned the task of valuing the U .S . stock market and uses data similar to the data that Smith uses for Canada. He decides, however, that the data should be transformed. He takes the natural log of the data and uses it in the following model:

Smith and Sims are concerned that the data for Canadian retail sales may be more appropriately modeled with an ARCH process. Smith states, that in order to find out, he would take the residuals from the original autoregressive model for Canadian retail sales and then square them.

Sims states that these residuals would then be regressed against the Canadian retail sales data using the

where e represents the residual terms from the original regression and X represents the Canadian retail sales data. If is statistically different from zero, then the regression model contains an ARCH process.

Smith also examines the quarterly inflation data for an emerging market over the past nine years. He models the data using an autoregressive model with a lag one independent variable which he finds is statistically different from zero. He wonders whether he should also include lag two and lag four terms, given the magnitude of the autocorrelations of the residuals shown in Exhibit 4, assuming a 5% significance level. The critical t-values, assuming a 5% significance level and 35 degrees of freedom, are 2.03 for a two-tail test and 1.69 for a one-tail test.

where: FF is the Federal Funds rate in the United States (US), and BY is the bond yield in the European Union (E) and Great Britain (B).

Before he runs this regression, he investigates the characteristics of the dependent and independent variables. He finds that the Federal Funds rate in the United States and the bond yield in Great Britain have a unit root but that the bond yield in the European Union does not. Furthermore, the Federal Funds rate in the United States and the bond yield in Great Britain are cointegrated but the Federal Funds rate in the United States and the bond yield in the European Union are not.

Which of the following models would be the best formulation for the Canadian retail sales data?

Show Suggested Answer Hide Answer
Suggested Answer: B

First, calculate the continuously compounded risk-free rate as ln( 1.040811) = 4% and then calculate the theoretically correct futures price as follows:

Then, compare the theoretical price to the observed market price: 1.035 - 1,025 = 10. The futures contract is overpriced. To take advantage of the arbitrage opportunity, the investor should sell the (overpriced) futures contract and buy the underlying asset (the equity index) using borrowed funds. Norris has suggested the opposite. (Study Session 16, LOS 59.f)


Contribute your Thoughts:

0/2000 characters
Ilene
4 months ago
Not sure if just adding lag two and four is enough, could complicate things.
upvoted 0 times
...
Aleta
4 months ago
Totally agree with checking for ARCH effects, it's crucial!
upvoted 0 times
...
Kenny
5 months ago
Wait, squaring the residuals? That sounds a bit off to me.
upvoted 0 times
...
Gaynell
5 months ago
I think Sims' log transformation for the U.S. data makes sense.
upvoted 0 times
...
Adolph
5 months ago
Smith's using lag one and lag twelve for the Canadian model, interesting choice!
upvoted 0 times
...
Aaron
5 months ago
I remember that cointegration is important for time series data, but I’m unsure how it affects the choice of model for Canadian retail sales. Should I focus more on the unit roots or the cointegration results?
upvoted 0 times
...
Mable
6 months ago
I feel like I should include lag two and lag four terms based on the autocorrelations, but I’m a bit confused about how to interpret the critical t-values. Do I need to compare them to both one-tail and two-tail tests?
upvoted 0 times
...
Alease
6 months ago
I think the ARCH process is relevant here, especially since they mentioned checking the residuals. I practiced a similar question, but I can't recall the exact steps for the regression of the squared residuals.
upvoted 0 times
...
Detra
6 months ago
I remember we discussed autoregressive models in class, but I'm not entirely sure how to determine which lag to include. Should I just look at the AIC or BIC values?
upvoted 0 times
...
Augustine
6 months ago
This seems like a tricky question, but I think I have a strategy. I'll start by reviewing the details of the autoregressive and ARCH models to understand the key differences. Then I'll evaluate how well each one fits the characteristics of the Canadian retail sales data described in the question.
upvoted 0 times
...
Luann
6 months ago
I'm a bit confused by all the different variables and models mentioned in the question. I'll need to take some time to really digest all the information and make sure I understand the implications of each approach before selecting an answer.
upvoted 0 times
...
Junita
6 months ago
The question is asking about the best model for the Canadian retail sales data, so I'll need to focus on understanding the details of the autoregressive and ARCH models described. The key will be determining which one is most appropriate based on the information given.
upvoted 0 times
...
Altha
6 months ago
This seems like a complex question with a lot of details to consider. I'll need to carefully review the information provided and think through the different models mentioned before deciding on the best approach.
upvoted 0 times
...
Remedios
6 months ago
Okay, I think I've got a handle on this. The question is asking us to evaluate the different models presented and select the best one for the Canadian retail sales data. I'll need to carefully consider the pros and cons of each approach and make a reasoned decision.
upvoted 0 times
...
Maile
6 months ago
Okay, I think I've got this. Based on the schema, the correct XML snippet to associate the device with name "SEP151515151515" would be Option B. The schema shows the device element nested under the addUser element, so that's the right structure.
upvoted 0 times
...
Dan
6 months ago
Hmm, this looks like a tricky OSPF question. I'll need to carefully review the topology and think through the different types of LSAs that R3 might advertise.
upvoted 0 times
...
Juliann
6 months ago
I feel a bit confused. Was it the financial audits department or the planning and scheduling department? I should have reviewed that outline again.
upvoted 0 times
...
Aliza
11 months ago
As an analyst, I'm always tempted to try the fanciest models, but sometimes the simplest approach is best. I think I'll go with Option A - it may not be the most sophisticated, but it seems to fit the data pretty well. Of course, I reserve the right to change my mind if the ARCH model turns out to be a real game-changer!
upvoted 0 times
Jacklyn
9 months ago
Let's go with Option A for now and see how it goes. We can always adjust later.
upvoted 0 times
...
Truman
9 months ago
I'm leaning towards Option A as well, it just seems to make sense.
upvoted 0 times
...
Kerrie
10 months ago
Yeah, I think sticking with Option A is a safe bet for now.
upvoted 0 times
...
Antonio
10 months ago
I agree, sometimes simpler is better. Option A seems like a good choice.
upvoted 0 times
...
...
Ashton
11 months ago
Ah, the age-old dilemma of model selection - do we go for simplicity or complexity? I say we flip a coin and let fate decide. Or maybe we should just ask the magic 8-ball, it's bound to give us a more reliable answer than we could come up with!
upvoted 0 times
Lore
9 months ago
Yeah, let's trust in option B for now and see how it plays out.
upvoted 0 times
...
Roxane
9 months ago
I agree, option B seems like a solid choice for the Canadian retail sales data.
upvoted 0 times
...
Apolonia
10 months ago
Let's go with option B, it seems like a good balance of simplicity and complexity.
upvoted 0 times
...
...
Lilli
11 months ago
Hmm, I'm not sure. Option B with the additional lag twelve variable might be worth considering, especially since the data has a seasonal component. But the ARCH model does seem like it could be the most appropriate based on the information provided.
upvoted 0 times
Janey
11 months ago
Considering the seasonal component, Option B with the lag twelve variable could be a good choice.
upvoted 0 times
...
Tracey
11 months ago
The ARCH model might be the most appropriate for the Canadian retail sales data.
upvoted 0 times
...
Dortha
11 months ago
Option B with the additional lag twelve variable could capture the seasonal component.
upvoted 0 times
...
...
Loren
12 months ago
I disagree, I think Option A - the simple autoregressive model with a lag one variable - is the best choice. The results look pretty good, and we don't need to overcomplicate things with an ARCH model unless it's really necessary.
upvoted 0 times
Eloisa
10 months ago
Yeah, no need to make it more complex if Option A works well.
upvoted 0 times
...
Galen
10 months ago
Option A seems like the most straightforward approach.
upvoted 0 times
...
Ena
11 months ago
I agree, keeping it simple with the autoregressive model makes sense.
upvoted 0 times
...
Lucina
11 months ago
I think Option A is the best choice. The results are solid.
upvoted 0 times
...
...
Latonia
12 months ago
I think the best model would be Option C - the ARCH model. The residuals from the original autoregressive model for Canadian retail sales show evidence of heteroscedasticity, so an ARCH model would be more appropriate to capture the time-varying volatility.
upvoted 0 times
...
Shawnda
12 months ago
What makes you think Option C is better than Option A?
upvoted 0 times
...
Susana
12 months ago
I disagree, I believe Option C is the best choice for modeling the Canadian retail sales data.
upvoted 0 times
...
Shawnda
1 year ago
I think Option A would be the best formulation for the Canadian retail sales data.
upvoted 0 times
...
Shaun
1 year ago
But Option A seems to have a more robust model based on the regression results.
upvoted 0 times
...
Edison
1 year ago
I disagree, I believe Option C is the best choice based on the data provided.
upvoted 0 times
...
Shaun
1 year ago
I think Option A would be the best formulation for the Canadian retail sales data.
upvoted 0 times
...

Save Cancel