# P1.T2. Quantitative Analysis

Practice questions for Quantitative Analysis: Econometrics, MCS, Volatility, Probability Distributions and VaR (Intro)

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1. ### P1.T2.300. Probability functions (Miller)

Hello @fccodart I just wanted to make sure that you read through all of the comments in this forum thread (there are 5 pages of discussions in this thread) to see if your question was already answered. The first question that was posted asks about the antiderivative formulas, and David...
Hello @fccodart I just wanted to make sure that you read through all of the comments in this forum thread (there are 5 pages of discussions in this thread) to see if your question was already...
Replies:
80
Views:
2,994
2. ### P1.T2.309. Probability Distributions I, Miller Chapter 4

@David Harper CFA FRM - thank you very very much for such a detailed answer. Now that I understand the difference between event and outcome, or permutation vs. combination, allow me to supplement my question as follows: Is it even possible to do question without doing a binomial tree? I.e. on exam day, is there a way to think about this in a way that we can "quickly" understand that the 7/5...
@David Harper CFA FRM - thank you very very much for such a detailed answer. Now that I understand the difference between event and outcome, or permutation vs. combination, allow me to supplement my question as follows: Is it even possible to do question without doing a binomial tree? I.e. on exam day, is there a way to think about this in a way that we can "quickly" understand that the 7/5...
@David Harper CFA FRM - thank you very very much for such a detailed answer. Now that I understand the difference between event and outcome, or permutation vs. combination, allow me to supplement my question as follows: Is it even possible to do question without doing a binomial tree? I.e. on...
@David Harper CFA FRM - thank you very very much for such a detailed answer. Now that I understand the difference between event and outcome, or permutation vs. combination, allow me to supplement...
Pam Gordon ... 2 3
Replies:
59
Views:
1,479
3. ### P1.T2.310. Probability Distributions II, Miller Chapter 4

Hi @verdi Your expression, var(x+y)=varX+varY+2covXY, is correct of course. But its general form, if we include constants (aka, weights) of 'a' and 'b' is given by var(aX + bY) = a^2*var(X) + b^2*var(Y) + 2*a*b*cov(X,Y); by general-special, I just mean that your expression is the "special case" where a = 1 and b = 1. In fact, this variance is a special case of the covariance and itself further...
Hi @verdi Your expression, var(x+y)=varX+varY+2covXY, is correct of course. But its general form, if we include constants (aka, weights) of 'a' and 'b' is given by var(aX + bY) = a^2*var(X) + b^2*var(Y) + 2*a*b*cov(X,Y); by general-special, I just mean that your expression is the "special case" where a = 1 and b = 1. In fact, this variance is a special case of the covariance and itself further...
Hi @verdi Your expression, var(x+y)=varX+varY+2covXY, is correct of course. But its general form, if we include constants (aka, weights) of 'a' and 'b' is given by var(aX + bY) = a^2*var(X) + b^2*var(Y) + 2*a*b*cov(X,Y); by general-special, I just mean that your expression is the "special case"...
Hi @verdi Your expression, var(x+y)=varX+varY+2covXY, is correct of course. But its general form, if we include constants (aka, weights) of 'a' and 'b' is given by var(aX + bY) = a^2*var(X) +...
Pam Gordon ... 2 3
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50
Views:
1,236
4. ### P1.T2.202. Variance of sum of random variables (Stock & Watson)

Hi @Arseniy Semiletenko Good point! In truth, it's a weakness of my question: I wrote this question 2012 (per the 2xx.x numbering) and, having improved my technique, I would not today write a question that has two valid answers to the self-contained question. It's not a "best practice." It's a corollary of a rule that I've employed in reviewing, and giving feedback on, GARP's own practice...
Hi @Arseniy Semiletenko Good point! In truth, it's a weakness of my question: I wrote this question 2012 (per the 2xx.x numbering) and, having improved my technique, I would not today write a question that has two valid answers to the self-contained question. It's not a "best practice." It's a corollary of a rule that I've employed in reviewing, and giving feedback on, GARP's own practice...
Hi @Arseniy Semiletenko Good point! In truth, it's a weakness of my question: I wrote this question 2012 (per the 2xx.x numbering) and, having improved my technique, I would not today write a question that has two valid answers to the self-contained question. It's not a "best practice." It's a...
Hi @Arseniy Semiletenko Good point! In truth, it's a weakness of my question: I wrote this question 2012 (per the 2xx.x numbering) and, having improved my technique, I would not today write a...
Replies:
61
Views:
1,187
5. ### P1.T2.209 T-statistic and confidence interval (Stock & Watson)

Hi @David Harper CFA FRM , thank you very much, yes this is perfectly clear now, I missed the difference between number of default and default rate (hence use of CLT). Thank you for the material, so so helpful. Also I apologize because I must have misread the thread and I realize now that I posted a question which you have extensively answered before. My bad Florence
Hi @David Harper CFA FRM , thank you very much, yes this is perfectly clear now, I missed the difference between number of default and default rate (hence use of CLT). Thank you for the material, so so helpful. Also I apologize because I must have misread the thread and I realize now that I posted a question which you have extensively answered before. My bad Florence
Hi @David Harper CFA FRM , thank you very much, yes this is perfectly clear now, I missed the difference between number of default and default rate (hence use of CLT). Thank you for the material, so so helpful. Also I apologize because I must have misread the thread and I realize now that I...
Hi @David Harper CFA FRM , thank you very much, yes this is perfectly clear now, I missed the difference between number of default and default rate (hence use of CLT). Thank you for the material,...
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71
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1,135
6. ### P1.T2.301. Miller's probability matrix

Hi @Jaskarn I don't think i follow, sorry. $6.00 is the exposure (aka, EAD) such that$6.00 * 3.0% equals the expected loss if LGD is zero (i.e., recovery is 100%). The basic formula, in percentage terms, is EL(%) = PD*LGD or EL = EAD*PD*LGD. In this case of question 301.2, the LGD distribution is given in dollars so its average is EAD*LGD = $6.00 * 66.67% =$4.00; if $4.00 is the average,... Hi @Jaskarn I don't think i follow, sorry.$6.00 is the exposure (aka, EAD) such that $6.00 * 3.0% equals the expected loss if LGD is zero (i.e., recovery is 100%). The basic formula, in percentage terms, is EL(%) = PD*LGD or EL = EAD*PD*LGD. In this case of question 301.2, the LGD distribution is given in dollars so its average is EAD*LGD =$6.00 * 66.67% = $4.00; if$4.00 is the average,...
Hi @Jaskarn I don't think i follow, sorry. $6.00 is the exposure (aka, EAD) such that$6.00 * 3.0% equals the expected loss if LGD is zero (i.e., recovery is 100%). The basic formula, in percentage terms, is EL(%) = PD*LGD or EL = EAD*PD*LGD. In this case of question 301.2, the LGD distribution...
Hi @Jaskarn I don't think i follow, sorry. $6.00 is the exposure (aka, EAD) such that$6.00 * 3.0% equals the expected loss if LGD is zero (i.e., recovery is 100%). The basic formula, in...
Fran ... 2
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36
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1,100
7. ### P1.T2.303 Mean and variance of continuous probability density functions (pdf) (Miller)

Thanks David. I was struggling in the beginning, but after redoing it and tried to understand the steps, it became more logical
Thanks David. I was struggling in the beginning, but after redoing it and tried to understand the steps, it became more logical
Thanks David. I was struggling in the beginning, but after redoing it and tried to understand the steps, it became more logical
Thanks David. I was struggling in the beginning, but after redoing it and tried to understand the steps, it became more logical
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50
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1,095
8. ### P1.T2.312. Mixture distributions (Miller)

Just to add a few more thoughts, the exam "could" ask you to use an obscure level of significance which would require you to retrieve a value from a z table. If this was the case, the exam would provide a snippet of the respective region of the z table. (I would add that this is a totally reasonable question in my mind). Also, memorizing the most common z's will help you but I don't think...
Just to add a few more thoughts, the exam "could" ask you to use an obscure level of significance which would require you to retrieve a value from a z table. If this was the case, the exam would provide a snippet of the respective region of the z table. (I would add that this is a totally reasonable question in my mind). Also, memorizing the most common z's will help you but I don't think...
Just to add a few more thoughts, the exam "could" ask you to use an obscure level of significance which would require you to retrieve a value from a z table. If this was the case, the exam would provide a snippet of the respective region of the z table. (I would add that this is a totally...
Just to add a few more thoughts, the exam "could" ask you to use an obscure level of significance which would require you to retrieve a value from a z table. If this was the case, the exam would...
Replies:
43
Views:
1,083
9. ### P1.T2.504. Copulas (Hull)

Hello The practice questions that David writes are focused around the learning objectives in the GARP curriculum, but many times, his questions are more difficult. He writes them at a higher level to ensure that our members understand the concepts in depth. So while this question may be more difficult than the questions that you will see on the exam, the concepts are still testable, as they...
Hello The practice questions that David writes are focused around the learning objectives in the GARP curriculum, but many times, his questions are more difficult. He writes them at a higher level to ensure that our members understand the concepts in depth. So while this question may be more difficult than the questions that you will see on the exam, the concepts are still testable, as they...
Hello The practice questions that David writes are focused around the learning objectives in the GARP curriculum, but many times, his questions are more difficult. He writes them at a higher level to ensure that our members understand the concepts in depth. So while this question may be more...
Hello The practice questions that David writes are focused around the learning objectives in the GARP curriculum, but many times, his questions are more difficult. He writes them at a higher...
Replies:
25
Views:
969
10. ### P1.T2.307. Skew and Kurtosis (Miller)

Hi @verdi Yes, nice catch of the typo (which I did miss). It should be either Σ [(xi - μ)^2 * pi] or 1/n*Σ (xi - μ)^2, as in Σ [(xi - μ)^2 * pi] = (1-3)^2*(1/3) + (2-3)^2*(1/3) +(6-3)^2*(1/3) = 4.67, but since each of the outcomes is equally likely this is the same as "un-distributing the 1/3" with 1/n*Σ (xi - μ)^2 = (1/3)* [(1-3)^2 + (2-3)^2 +(6-3)^2] = 4.67. Thank you for your attention to...
Hi @verdi Yes, nice catch of the typo (which I did miss). It should be either Σ [(xi - μ)^2 * pi] or 1/n*Σ (xi - μ)^2, as in Σ [(xi - μ)^2 * pi] = (1-3)^2*(1/3) + (2-3)^2*(1/3) +(6-3)^2*(1/3) = 4.67, but since each of the outcomes is equally likely this is the same as "un-distributing the 1/3" with 1/n*Σ (xi - μ)^2 = (1/3)* [(1-3)^2 + (2-3)^2 +(6-3)^2] = 4.67. Thank you for your attention to...
Hi @verdi Yes, nice catch of the typo (which I did miss). It should be either Σ [(xi - μ)^2 * pi] or 1/n*Σ (xi - μ)^2, as in Σ [(xi - μ)^2 * pi] = (1-3)^2*(1/3) + (2-3)^2*(1/3) +(6-3)^2*(1/3) = 4.67, but since each of the outcomes is equally likely this is the same as "un-distributing the...
Hi @verdi Yes, nice catch of the typo (which I did miss). It should be either Σ [(xi - μ)^2 * pi] or 1/n*Σ (xi - μ)^2, as in Σ [(xi - μ)^2 * pi] = (1-3)^2*(1/3) + (2-3)^2*(1/3) +(6-3)^2*(1/3) =...
Fran ... 2
Replies:
32
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949
11. ### P1.T2.503. One-factor model (Hull)

@hellohi, This is how I have solved: e1=z1= -0.88 e2= pz1 + z2*sqrt(1-p^2) e2= [0.70*(-0.88)] + [0.63*sqrt(1-(0.7)^2) e2= -0.16609 U= Mean + (SD*e1) U= 5 + [3*(-0.88)] U= 2.36 V= Mean + (SD*e2) V= 10 + [6*(-0.16609)] V= 9.00346 Thanks, Rajiv
@hellohi, This is how I have solved: e1=z1= -0.88 e2= pz1 + z2*sqrt(1-p^2) e2= [0.70*(-0.88)] + [0.63*sqrt(1-(0.7)^2) e2= -0.16609 U= Mean + (SD*e1) U= 5 + [3*(-0.88)] U= 2.36 V= Mean + (SD*e2) V= 10 + [6*(-0.16609)] V= 9.00346 Thanks, Rajiv
@hellohi, This is how I have solved: e1=z1= -0.88 e2= pz1 + z2*sqrt(1-p^2) e2= [0.70*(-0.88)] + [0.63*sqrt(1-(0.7)^2) e2= -0.16609 U= Mean + (SD*e1) U= 5 + [3*(-0.88)] U= 2.36 V= Mean + (SD*e2) V= 10 + [6*(-0.16609)] V= 9.00346 Thanks, Rajiv
@hellohi, This is how I have solved: e1=z1= -0.88 e2= pz1 + z2*sqrt(1-p^2) e2= [0.70*(-0.88)] + [0.63*sqrt(1-(0.7)^2) e2= -0.16609 U= Mean + (SD*e1) U= 5 + [3*(-0.88)] U= 2.36 V= Mean +...
Replies:
20
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910
12. ### L1.T2.111 Binomial & Poisson (Rachev)

Hi @s3filin It's a terrific observation The Poisson can approximate the binomial (see which applies when n*p is low; in this case n*p is not super low but it's getting there). And, indeed: =BINOM.DIST(X = 5, trials = 500, p = 1%, pmf = false) = 17.63510451%, and =POISSON.DIST(X = 5, mean = 1%*500, pmf - false) = 17.54673698%. Their cumulative (CDF) is even closer: =BINOM.DIST(X = 5,...
Hi @s3filin It's a terrific observation The Poisson can approximate the binomial (see which applies when n*p is low; in this case n*p is not super low but it's getting there). And, indeed: =BINOM.DIST(X = 5, trials = 500, p = 1%, pmf = false) = 17.63510451%, and =POISSON.DIST(X = 5, mean = 1%*500, pmf - false) = 17.54673698%. Their cumulative (CDF) is even closer: =BINOM.DIST(X = 5,...
Hi @s3filin It's a terrific observation The Poisson can approximate the binomial (see which applies when n*p is low; in this case n*p is not super low but it's getting there). And, indeed: =BINOM.DIST(X = 5, trials = 500, p = 1%, pmf = false) = 17.63510451%, and =POISSON.DIST(X = 5, mean =...
Hi @s3filin It's a terrific observation The Poisson can approximate the binomial (see which applies when n*p is low; in this case n*p is not super low but it's getting there). And,...
Replies:
44
Views:
796
13. ### P1.T2.305. Minimum variance hedge (Miller)

It absolutely helps. Thank you!
It absolutely helps. Thank you!
It absolutely helps. Thank you!
It absolutely helps. Thank you!
Fran ... 2
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24
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772
14. ### P1.T2.304. Covariance (Miller)

Ohh I was having this same doubt... thanks.
Ohh I was having this same doubt... thanks.
Ohh I was having this same doubt... thanks.
Ohh I was having this same doubt... thanks.
Fran ... 2
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28
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758
15. ### P1.T2.306. Calculate the mean and variance of sums of variables. (Miller)

Hi @verdi for the same reason that var(aF) = a^2*var(F): variance(constant*X) = constant^2*variance(X). In the term sqrt(1-a^2)*e, "e" is the random variable and sqrt(1-a^2) is itself a constant, so this is effectively var[a*e] where a = sqrt(1-a^2), such that this constant is squared: sqrt(1-a^2)*e = [sqrt(1-a^2)]^2*var(e) = (1-a^2)*var(e). If it all seems too convenient, please note this...
Hi @verdi for the same reason that var(aF) = a^2*var(F): variance(constant*X) = constant^2*variance(X). In the term sqrt(1-a^2)*e, "e" is the random variable and sqrt(1-a^2) is itself a constant, so this is effectively var[a*e] where a = sqrt(1-a^2), such that this constant is squared: sqrt(1-a^2)*e = [sqrt(1-a^2)]^2*var(e) = (1-a^2)*var(e). If it all seems too convenient, please note this...
Hi @verdi for the same reason that var(aF) = a^2*var(F): variance(constant*X) = constant^2*variance(X). In the term sqrt(1-a^2)*e, "e" is the random variable and sqrt(1-a^2) is itself a constant, so this is effectively var[a*e] where a = sqrt(1-a^2), such that this constant is squared:...
Hi @verdi for the same reason that var(aF) = a^2*var(F): variance(constant*X) = constant^2*variance(X). In the term sqrt(1-a^2)*e, "e" is the random variable and sqrt(1-a^2) is itself a constant,...
Fran ... 2 3
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42
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712
16. ### P1.T2.502. Covariance updates with EWMA and GARCH(1,1) models (Hull)

Hi @Spinozzi That's a fair observation. I did parrot Hull's language here, such that he does refer to these given assumptions as "current daily volatilties" (see emphasized text below; which is solved above in the XLS snapshot on the column next to BT 502.2). I also cross-checked his usage in OFOD 10th edition and he similarly refers to these assumptions as "current daily volatilities." (e.g.,...
Hi @Spinozzi That's a fair observation. I did parrot Hull's language here, such that he does refer to these given assumptions as "current daily volatilties" (see emphasized text below; which is solved above in the XLS snapshot on the column next to BT 502.2). I also cross-checked his usage in OFOD 10th edition and he similarly refers to these assumptions as "current daily volatilities." (e.g.,...
Hi @Spinozzi That's a fair observation. I did parrot Hull's language here, such that he does refer to these given assumptions as "current daily volatilties" (see emphasized text below; which is solved above in the XLS snapshot on the column next to BT 502.2). I also cross-checked his usage in...
Hi @Spinozzi That's a fair observation. I did parrot Hull's language here, such that he does refer to these given assumptions as "current daily volatilties" (see emphasized text below; which is...
Replies:
23
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680
17. ### P1.T2.206. Variance of sample average (Stock & Watson)

I am asking kind of dumb question, but where is this formula in the Miller Chapter (please tell me reference in David's Pdf)
I am asking kind of dumb question, but where is this formula in the Miller Chapter (please tell me reference in David's Pdf)
I am asking kind of dumb question, but where is this formula in the Miller Chapter (please tell me reference in David's Pdf)
I am asking kind of dumb question, but where is this formula in the Miller Chapter (please tell me reference in David's Pdf)
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24
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665
18. ### P1.T2.212. Difference between two means (Stock & Watson)

That was a long message to type on a phone - got kind of tired towards the end!
That was a long message to type on a phone - got kind of tired towards the end!
That was a long message to type on a phone - got kind of tired towards the end!
That was a long message to type on a phone - got kind of tired towards the end!
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34
Views:
650
19. ### L1.T2.104 Exponentially weighted moving average (EWMA) (Hull)

@Deepak Chitnis and @David Harper CFA FRM CIPM thanks for your replies...I will make sure I keep a special eye out as to whether the question mentions simple vs LN returns. If the question mentions neither, I think I shall plumb for the LN option as that just feels more "right" to me. But hopefully it won't be too much of an issue.
@Deepak Chitnis and @David Harper CFA FRM CIPM thanks for your replies...I will make sure I keep a special eye out as to whether the question mentions simple vs LN returns. If the question mentions neither, I think I shall plumb for the LN option as that just feels more "right" to me. But hopefully it won't be too much of an issue.
@Deepak Chitnis and @David Harper CFA FRM CIPM thanks for your replies...I will make sure I keep a special eye out as to whether the question mentions simple vs LN returns. If the question mentions neither, I think I shall plumb for the LN option as that just feels more "right" to me. But...
@Deepak Chitnis and @David Harper CFA FRM CIPM thanks for your replies...I will make sure I keep a special eye out as to whether the question mentions simple vs LN returns. If the question...
Replies:
27
Views:
536
20. ### P1.T2.311. Probability Distributions III, Miller

Hi @s3filin This is a typical Monte Carlo assumption: that certain risk factors are (at least a little bit) correlated. This would be used any time we want correlated normals in a Monte Carlo Simulation; it's almost not too much to say that independence (i.e., zero correlation) would be the unusual assumption. But it's super-super-easy to generate non-correlated normals, so the point is to...
Hi @s3filin This is a typical Monte Carlo assumption: that certain risk factors are (at least a little bit) correlated. This would be used any time we want correlated normals in a Monte Carlo Simulation; it's almost not too much to say that independence (i.e., zero correlation) would be the unusual assumption. But it's super-super-easy to generate non-correlated normals, so the point is to...
Hi @s3filin This is a typical Monte Carlo assumption: that certain risk factors are (at least a little bit) correlated. This would be used any time we want correlated normals in a Monte Carlo Simulation; it's almost not too much to say that independence (i.e., zero correlation) would be the...
Hi @s3filin This is a typical Monte Carlo assumption: that certain risk factors are (at least a little bit) correlated. This would be used any time we want correlated normals in a Monte Carlo...
Replies:
25
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508
21. ### Quiz-T2P1.T2.405. Distributions I

Hi @otcfin Per this recent thread here is my summary table on the use of normal Z versus student's t, the choice essentially depends on whether we know the population variance (i.e., known variance justifies the normal, but unknown variance estimates the population by assuming the sample variance which consumes a d.f. and warrants the more conservative student's t): Re t-statistic degrees...
Hi @otcfin Per this recent thread here is my summary table on the use of normal Z versus student's t, the choice essentially depends on whether we know the population variance (i.e., known variance justifies the normal, but unknown variance estimates the population by assuming the sample variance which consumes a d.f. and warrants the more conservative student's t): Re t-statistic degrees...
Hi @otcfin Per this recent thread here is my summary table on the use of normal Z versus student's t, the choice essentially depends on whether we know the population variance (i.e., known variance justifies the normal, but unknown variance estimates the population by assuming the sample...
Hi @otcfin Per this recent thread here is my summary table on the use of normal Z versus student's t, the choice essentially depends on whether we know the population variance (i.e., known...
Replies:
18
Views:
495
22. ### P1.T2.314. Miller's one- and two-tailed hypotheses

Thank you @David Harper CFA FRM !
Thank you @David Harper CFA FRM !
Thank you @David Harper CFA FRM !
Thank you @David Harper CFA FRM !
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26
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460

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25
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442
24. ### P1.T2.208. Sample mean estimators (Stock & Watson)

Hi David, I was just referring to the previous discussion to give better understanding to my question Thanks a lot for your time and patience. Praveen
Hi David, I was just referring to the previous discussion to give better understanding to my question Thanks a lot for your time and patience. Praveen
Hi David, I was just referring to the previous discussion to give better understanding to my question Thanks a lot for your time and patience. Praveen
Hi David, I was just referring to the previous discussion to give better understanding to my question Thanks a lot for your time and patience. Praveen
Replies:
33
Views:
442
25. ### P1.T2.501. More Bayes Theorem (Miller)

Thanks David. If this one will be tested, I will try to get used to using the function
Thanks David. If this one will be tested, I will try to get used to using the function
Thanks David. If this one will be tested, I will try to get used to using the function
Thanks David. If this one will be tested, I will try to get used to using the function
Replies:
19
Views:
436
26. ### L1.T2.103 Weighting schemes to estimate volatility (Hull)

Hi @s3filin Great question and, yes, I am indeed saying that "Beta [in GARCH] is a decay factor and is analogous to lambda in EWMA." Hull actually shows this specifically in Chapter 23.4; I copied it below. In this way, GARCH β is analogous to EWMA λ; and GARCH α is analogous to EWMA's (1-λ) so I would not say--and hopefully did not anywhere say something like "what's lambda for EWMA is...
Hi @s3filin Great question and, yes, I am indeed saying that "Beta [in GARCH] is a decay factor and is analogous to lambda in EWMA." Hull actually shows this specifically in Chapter 23.4; I copied it below. In this way, GARCH β is analogous to EWMA λ; and GARCH α is analogous to EWMA's (1-λ) so I would not say--and hopefully did not anywhere say something like "what's lambda for EWMA is...
Hi @s3filin Great question and, yes, I am indeed saying that "Beta [in GARCH] is a decay factor and is analogous to lambda in EWMA." Hull actually shows this specifically in Chapter 23.4; I copied it below. In this way, GARCH β is analogous to EWMA λ; and GARCH α is analogous to EWMA's (1-λ) so...
Hi @s3filin Great question and, yes, I am indeed saying that "Beta [in GARCH] is a decay factor and is analogous to lambda in EWMA." Hull actually shows this specifically in Chapter 23.4; I copied...
Replies:
11
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427
27. ### Quiz-T2P1.T2.403. Probabilities

@champ123 @David Harper CFA FRM This has been fixed in both the pdf and the quiz.
@champ123 @David Harper CFA FRM This has been fixed in both the pdf and the quiz.
@champ123 @David Harper CFA FRM This has been fixed in both the pdf and the quiz.
@champ123 @David Harper CFA FRM This has been fixed in both the pdf and the quiz.
Replies:
16
Views:
411
28. ### PQ-T2P1.T2.317. Continuous distributions (Topic review)

Hello @Gdb Thank you for pointing this out. I've fixed this error in the study planner. Nicole
Hello @Gdb Thank you for pointing this out. I've fixed this error in the study planner. Nicole
Hello @Gdb Thank you for pointing this out. I've fixed this error in the study planner. Nicole
Hello @Gdb Thank you for pointing this out. I've fixed this error in the study planner. Nicole
Replies:
8
Views:
411
29. ### L1.T2.108 Volatility forecast with GARCH(1,1) (Hull)

Hi @Tania Pereira Right, either is acceptable and, in the case of question 108.3 above, it makes a difference: the given answer is 2.363% but if we instead computed a discrete daily return (i.e., 11.052/10 - 1 = 3.83%) then the 10-day volatility forecast is 2.429%, a difference of 0.066%. That's why this older question of mine is clearly imprecise (sorry): the question needs to specify that...
Hi @Tania Pereira Right, either is acceptable and, in the case of question 108.3 above, it makes a difference: the given answer is 2.363% but if we instead computed a discrete daily return (i.e., 11.052/10 - 1 = 3.83%) then the 10-day volatility forecast is 2.429%, a difference of 0.066%. That's why this older question of mine is clearly imprecise (sorry): the question needs to specify that...
Hi @Tania Pereira Right, either is acceptable and, in the case of question 108.3 above, it makes a difference: the given answer is 2.363% but if we instead computed a discrete daily return (i.e., 11.052/10 - 1 = 3.83%) then the 10-day volatility forecast is 2.429%, a difference of 0.066%. That's...
Hi @Tania Pereira Right, either is acceptable and, in the case of question 108.3 above, it makes a difference: the given answer is 2.363% but if we instead computed a discrete daily return (i.e.,...
Replies:
26
Views:
400
30. ### P1.T2.204. Joint, marginal, and conditional probability functions (Stock & Watson)

Hi Melody (@superpocoyo ) Here is the spreadsheet @ Please note that, in my response to mastvikas above, I had a typo which I've now corrected. It should read: (10 - 29.38)^2*(0.05/.32) = 58.65 105.859 is the conditional variance which determines the answer of 10.3 (the conditional standard deviation). I think the key here is to realize that, after we grok the conditionality, we are...
Hi Melody (@superpocoyo ) Here is the spreadsheet @ Please note that, in my response to mastvikas above, I had a typo which I've now corrected. It should read: (10 - 29.38)^2*(0.05/.32) = 58.65 105.859 is the conditional variance which determines the answer of 10.3 (the conditional standard deviation). I think the key here is to realize that, after we grok the conditionality, we are...
Hi Melody (@superpocoyo ) Here is the spreadsheet @ Please note that, in my response to mastvikas above, I had a typo which I've now corrected. It should read: (10 - 29.38)^2*(0.05/.32) = 58.65 105.859 is the conditional variance which determines the answer of 10.3 (the conditional standard...
Hi Melody (@superpocoyo ) Here is the spreadsheet @ Please note that, in my response to mastvikas above, I had a typo which I've now corrected. It should read: (10 - 29.38)^2*(0.05/.32) =...
Replies:
10
Views:
395