P1.T2. Quantitative Analysis

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

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  1. bbeckett

    P1.T2.305. Minimum variance hedge (Miller)

    Thanks David! I did just read that update...fortunately calc is slowly coming back to me with each example. Based on Bill's comments it would seem some other prep providers I have had access to may be light in this area. Thanks for the deep dive on the great, albeit challenging, questions!
    Thanks David! I did just read that update...fortunately calc is slowly coming back to me with each example. Based on Bill's comments it would seem some other prep providers I have had access to may be light in this area. Thanks for the deep dive on the great, albeit challenging, questions!
    Thanks David! I did just read that update...fortunately calc is slowly coming back to me with each example. Based on Bill's comments it would seem some other prep providers I have had access to may be light in this area. Thanks for the deep dive on the great, albeit challenging, questions!
    Thanks David! I did just read that update...fortunately calc is slowly coming back to me with each example. Based on Bill's comments it would seem some other prep providers I have had access to...
    Replies:
    4
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    31
  2. cabrown085

    Variance and Covariance Calculation Clarification

    Hi David, Thanks! I work in Excel every day so being able to look at the numbers was a big help. What I was describing in the first part can be summed up as: Pr*(X-µ)^2 The second equation can be described as: Pr*X^2-(sum(Pr*X))^2. sum(Pr*X) = µ What you were showing in the second example was with samples it may be difficult to assign a true distribution, so instead for a sample mean, you...
    Hi David, Thanks! I work in Excel every day so being able to look at the numbers was a big help. What I was describing in the first part can be summed up as: Pr*(X-µ)^2 The second equation can be described as: Pr*X^2-(sum(Pr*X))^2. sum(Pr*X) = µ What you were showing in the second example was with samples it may be difficult to assign a true distribution, so instead for a sample mean, you...
    Hi David, Thanks! I work in Excel every day so being able to look at the numbers was a big help. What I was describing in the first part can be summed up as: Pr*(X-µ)^2 The second equation can be described as: Pr*X^2-(sum(Pr*X))^2. sum(Pr*X) = µ What you were showing in the second example was...
    Hi David, Thanks! I work in Excel every day so being able to look at the numbers was a big help. What I was describing in the first part can be summed up as: Pr*(X-µ)^2 The second equation can...
    Replies:
    3
    Views:
    22
  3. cabrown085

    Uses of the Probability Density Function versus the Cumulative Distribution Function

    a discrete distribution has a pmf (probability mass function) instead of a prob density function (pdf) which is its continuous analog. An easy example of pmf/CDF is a fair six-sided die: the CDF is F(X) = X/6; i.e., the probability of rolling a three or less is 3/6 = 50% the pmf is the derivative: if F(X) = 1/6*x, then f(X) = F'(X) = 1/6; ie the pmf of a fair die is f(x) = 1/6 if f(x) = ax +...
    a discrete distribution has a pmf (probability mass function) instead of a prob density function (pdf) which is its continuous analog. An easy example of pmf/CDF is a fair six-sided die: the CDF is F(X) = X/6; i.e., the probability of rolling a three or less is 3/6 = 50% the pmf is the derivative: if F(X) = 1/6*x, then f(X) = F'(X) = 1/6; ie the pmf of a fair die is f(x) = 1/6 if f(x) = ax +...
    a discrete distribution has a pmf (probability mass function) instead of a prob density function (pdf) which is its continuous analog. An easy example of pmf/CDF is a fair six-sided die: the CDF is F(X) = X/6; i.e., the probability of rolling a three or less is 3/6 = 50% the pmf is the...
    a discrete distribution has a pmf (probability mass function) instead of a prob density function (pdf) which is its continuous analog. An easy example of pmf/CDF is a fair six-sided die: the CDF...
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    27
  4. Nicole Manley

    P1.T2.602. Bootstrapping (Brooks)

    a GARCH process is covered in the readings.... Simulations are used to produce samples from distributions that are not parametric or not in "closed form" or, perhaps better, simulations can be used to generate samples from parametric distributions when actual samples are difficult to obtain! Imagine a simulation of earthquakes or flood levels or survival in space.....
    a GARCH process is covered in the readings.... Simulations are used to produce samples from distributions that are not parametric or not in "closed form" or, perhaps better, simulations can be used to generate samples from parametric distributions when actual samples are difficult to obtain! Imagine a simulation of earthquakes or flood levels or survival in space.....
    a GARCH process is covered in the readings.... Simulations are used to produce samples from distributions that are not parametric or not in "closed form" or, perhaps better, simulations can be used to generate samples from parametric distributions when actual samples are difficult to obtain! ...
    a GARCH process is covered in the readings.... Simulations are used to produce samples from distributions that are not parametric or not in "closed form" or, perhaps better, simulations can be...
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    69
  5. Nicole Manley

    P1.T2.601. Variance reduction techniques (Brooks)

    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is effective. Describe the benefits of reusing sets of random number draws across Monte Carlo experiments and how to reuse them. Questions: 601.1. Betty is an analyst using Monte Carlo simulation to...
    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is effective. Describe the benefits of reusing sets of random number draws across Monte Carlo experiments and how to reuse them. Questions: 601.1. Betty is an analyst using Monte Carlo simulation to...
    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is effective. Describe the benefits of reusing sets of random number draws across Monte Carlo...
    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is...
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    0
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    49
  6. Nicole Manley

    P1.T2.600. Monte Carlo simulation, sampling error (Brooks)

    Thank you @QuantMan2318 , nice reasoning! @ (cc [USER=27903]@Nicole Manley ) The answer is given correctly as (C) which is false. But there was a typo, consistent with the text given, it should read "In regard to true (A), (B), and (D), ..." You might notice that the explanation itemizes each of the TRUE (A), (B), and (D), specifically:
    Thank you @QuantMan2318 , nice reasoning! @ (cc [USER=27903]@Nicole Manley ) The answer is given correctly as (C) which is false. But there was a typo, consistent with the text given, it should read "In regard to true (A), (B), and (D), ..." You might notice that the explanation itemizes each of the TRUE (A), (B), and (D), specifically:
    Thank you @QuantMan2318 , nice reasoning! @ (cc [USER=27903]@Nicole Manley ) The answer is given correctly as (C) which is false. But there was a typo, consistent with the text given, it should read "In regard to true (A), (B), and (D), ..." You might notice that the explanation itemizes each...
    Thank you @QuantMan2318 , nice reasoning! @ (cc [USER=27903]@Nicole Manley ) The answer is given correctly as (C) which is false. But there was a typo, consistent with the text given, it should...
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    87
  7. Nicole Manley

    P1.T2.512. Autoregressive moving average (ARMA) processes

    Learning outcomes: Define and describe the properties of the autoregressive moving average (ARMA) process. Describe the application of AR and ARMA processes. Questions: 512.1. Each of the following is a motivating for an autoregressive moving average (ARMA) process EXCEPT which is not? a. AR processes observed subject to measurement error also turn out to be ARMA processes b. When we need...
    Learning outcomes: Define and describe the properties of the autoregressive moving average (ARMA) process. Describe the application of AR and ARMA processes. Questions: 512.1. Each of the following is a motivating for an autoregressive moving average (ARMA) process EXCEPT which is not? a. AR processes observed subject to measurement error also turn out to be ARMA processes b. When we need...
    Learning outcomes: Define and describe the properties of the autoregressive moving average (ARMA) process. Describe the application of AR and ARMA processes. Questions: 512.1. Each of the following is a motivating for an autoregressive moving average (ARMA) process EXCEPT which is not? a. AR...
    Learning outcomes: Define and describe the properties of the autoregressive moving average (ARMA) process. Describe the application of AR and ARMA processes. Questions: 512.1. Each of the...
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    0
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    67
  8. David Harper CFA FRM

    P1.T2.511. First-order autoregressive, AR(1), process

    [USER=38486]@ Yes, if you look at the GARP curriculum for this year, you will see that these learning objectives are still under Topic 2, Reading 16, Diebold, Chapter 8. Thank you, Nicole
    [USER=38486]@ Yes, if you look at the GARP curriculum for this year, you will see that these learning objectives are still under Topic 2, Reading 16, Diebold, Chapter 8. Thank you, Nicole
    [USER=38486]@ Yes, if you look at the GARP curriculum for this year, you will see that these learning objectives are still under Topic 2, Reading 16, Diebold, Chapter 8. Thank you, Nicole
    [USER=38486]@ Yes, if you look at the GARP curriculum for this year, you will see that these learning objectives are still under Topic 2, Reading 16, Diebold, Chapter 8. Thank you, Nicole
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  9. Nicole Manley

    P1.T2.510. First-order and general finite-order moving average process, MA(1) and MA(q)

    If the roots are real and not complex, I believe.
    If the roots are real and not complex, I believe.
    If the roots are real and not complex, I believe.
    If the roots are real and not complex, I believe.
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    117
  10. Nicole Manley

    P1.T2.509. Box-Pierce and Ljung-Box Q-statistics

    Hi Joyce, Wonder how I made a mistake - yes, you are right, I was looking at Chi-square 95%, 24 instead of Chi-square 5%, 24 = 36.415! Thanks a tonne:) Jayanthi
    Hi Joyce, Wonder how I made a mistake - yes, you are right, I was looking at Chi-square 95%, 24 instead of Chi-square 5%, 24 = 36.415! Thanks a tonne:) Jayanthi
    Hi Joyce, Wonder how I made a mistake - yes, you are right, I was looking at Chi-square 95%, 24 instead of Chi-square 5%, 24 = 36.415! Thanks a tonne:) Jayanthi
    Hi Joyce, Wonder how I made a mistake - yes, you are right, I was looking at Chi-square 95%, 24 instead of Chi-square 5%, 24 = 36.415! Thanks a tonne:) Jayanthi
    Replies:
    3
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    108
  11. Nicole Manley

    P1.T2.508. Wold's theorem

    Learning outcomes: Describe Wold’s theorem. Define a general linear process. Relate rational distributed lags to Wold’s theorem Questions: 508.1. Wold's representation theorem points to an appropriate model for a covariance stationary residual such that: a. Any autoregressive process of (p) order can be expressed as a rational polynomial of lagged errors b. Any purely nondeterministic...
    Learning outcomes: Describe Wold’s theorem. Define a general linear process. Relate rational distributed lags to Wold’s theorem Questions: 508.1. Wold's representation theorem points to an appropriate model for a covariance stationary residual such that: a. Any autoregressive process of (p) order can be expressed as a rational polynomial of lagged errors b. Any purely nondeterministic...
    Learning outcomes: Describe Wold’s theorem. Define a general linear process. Relate rational distributed lags to Wold’s theorem Questions: 508.1. Wold's representation theorem points to an appropriate model for a covariance stationary residual such that: a. Any autoregressive process of (p)...
    Learning outcomes: Describe Wold’s theorem. Define a general linear process. Relate rational distributed lags to Wold’s theorem Questions: 508.1. Wold's representation theorem points to an...
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    0
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    110
  12. Nicole Manley

    P1.T2.507. White noise

    Learning outcomes: Define white noise, describe independent white noise and normal (Gaussian) white noise. Explain the characteristics of the dynamic structure of white noise. Explain how a lag operator works. Questions: 507.1. In regard to white noise, each of the following statements is true EXCEPT which is false? a. If a process is zero-mean white noise, then is must be Gaussian white...
    Learning outcomes: Define white noise, describe independent white noise and normal (Gaussian) white noise. Explain the characteristics of the dynamic structure of white noise. Explain how a lag operator works. Questions: 507.1. In regard to white noise, each of the following statements is true EXCEPT which is false? a. If a process is zero-mean white noise, then is must be Gaussian white...
    Learning outcomes: Define white noise, describe independent white noise and normal (Gaussian) white noise. Explain the characteristics of the dynamic structure of white noise. Explain how a lag operator works. Questions: 507.1. In regard to white noise, each of the following statements is true...
    Learning outcomes: Define white noise, describe independent white noise and normal (Gaussian) white noise. Explain the characteristics of the dynamic structure of white noise. Explain how a lag...
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    0
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    96
  13. Nicole Manley

    P1.T2.506. Covariance stationary time series

    Highly appreciate if you can paste the definition here related to 506.3 please.
    Highly appreciate if you can paste the definition here related to 506.3 please.
    Highly appreciate if you can paste the definition here related to 506.3 please.
    Highly appreciate if you can paste the definition here related to 506.3 please.
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  14. Nicole Manley

    P1.T2.505. Model selection criteria (Diebold)

    Hi @DTu Yes, but depending on the author, (k) can is sometimes defined as the number of independent variables or the number of parameters. For example, consider y = b + m1*x1 + m2*x2 + m3*x3 + e, is a regression model with three independent variables (x1, x2, x3), four total variables (including), and four parameters (slope b, m1, m2, m3). The degrees of freedom, df = n-4 because four...
    Hi @DTu Yes, but depending on the author, (k) can is sometimes defined as the number of independent variables or the number of parameters. For example, consider y = b + m1*x1 + m2*x2 + m3*x3 + e, is a regression model with three independent variables (x1, x2, x3), four total variables (including), and four parameters (slope b, m1, m2, m3). The degrees of freedom, df = n-4 because four...
    Hi @DTu Yes, but depending on the author, (k) can is sometimes defined as the number of independent variables or the number of parameters. For example, consider y = b + m1*x1 + m2*x2 + m3*x3 + e, is a regression model with three independent variables (x1, x2, x3), four total variables...
    Hi @DTu Yes, but depending on the author, (k) can is sometimes defined as the number of independent variables or the number of parameters. For example, consider y = b + m1*x1 + m2*x2 + m3*x3 + e,...
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    179
  15. Nicole Manley

    P1.T2.504. Copulas (Hull)

    dear @David Harper CFA FRM Jayanthi Sankaran[/USER] please, as I know the subjects outside of learning objectives are not asked for the actual exam, and I read the learning objevtives for this reading (Hull) and I found this subject is not included, but lately I visited again the reading in the original book and I became suspect that this subject include in the learning objectives under one...
    dear @David Harper CFA FRM Jayanthi Sankaran[/USER] please, as I know the subjects outside of learning objectives are not asked for the actual exam, and I read the learning objevtives for this reading (Hull) and I found this subject is not included, but lately I visited again the reading in the original book and I became suspect that this subject include in the learning objectives under one...
    dear @David Harper CFA FRM Jayanthi Sankaran[/USER] please, as I know the subjects outside of learning objectives are not asked for the actual exam, and I read the learning objevtives for this reading (Hull) and I found this subject is not included, but lately I visited again the reading in...
    dear @David Harper CFA FRM Jayanthi Sankaran[/USER] please, as I know the subjects outside of learning objectives are not asked for the actual exam, and I read the learning objevtives for this...
    Replies:
    25
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    551
  16. Nicole Manley

    P1.T2.503. One-factor model (Hull)

    Hi @bpdulog I apologize for the confusion, I inadvertently posted from the wrong Hull text such that I posted the Factor model (which applies to questions 503.2 and 503.3, while 503.1 refers to correlated normal random variables). They are essentially similar but there is a difference. Superficially, the factor model generates a vector (i.e., a single column) of random standard normals U(1),...
    Hi @bpdulog I apologize for the confusion, I inadvertently posted from the wrong Hull text such that I posted the Factor model (which applies to questions 503.2 and 503.3, while 503.1 refers to correlated normal random variables). They are essentially similar but there is a difference. Superficially, the factor model generates a vector (i.e., a single column) of random standard normals U(1),...
    Hi @bpdulog I apologize for the confusion, I inadvertently posted from the wrong Hull text such that I posted the Factor model (which applies to questions 503.2 and 503.3, while 503.1 refers to correlated normal random variables). They are essentially similar but there is a difference....
    Hi @bpdulog I apologize for the confusion, I inadvertently posted from the wrong Hull text such that I posted the Factor model (which applies to questions 503.2 and 503.3, while 503.1 refers to...
    Replies:
    15
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    394
  17. Nicole Manley

    P1.T2.502. Covariance updates with EWMA and GARCH(1,1) models

    that helps much .....thanks a lot dear deepak....;)
    that helps much .....thanks a lot dear deepak....;)
    that helps much .....thanks a lot dear deepak....;)
    that helps much .....thanks a lot dear deepak....;)
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    322
  18. Nicole Manley

    P1.T2.501. More Bayes Theorem (Miller)

    Thank you very much Ami. I mangled the formula.
    Thank you very much Ami. I mangled the formula.
    Thank you very much Ami. I mangled the formula.
    Thank you very much Ami. I mangled the formula.
    Replies:
    7
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    212
  19. Nicole Manley

    P1.T2.500. Bayes theorem

    Testing Amazon link
    Testing Amazon link
    Testing Amazon link
    Testing Amazon link
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    312
  20. Nicole Manley

    P1.T2.409 Volatility, GARCH(1,1) and EWMA

    Per @Robert Paterson 's correction, the first bullet under 409.2.A corrected to read: In regard to (a), this is FALSE: because the weights sum to one (i.e., alpha + beta + gamma = 1.0) and omega = long-run variance*gamma, the long-run volatility = SQRT[omega/gamma] = sqrt[omega/(1 - alpha - gamma)] = sqrt[0.0000960/(1 - 0.060 - 0.880)] = sqrt[0.0000960/0.060] = 4.0% (+1 star for @Robert...
    Per @Robert Paterson 's correction, the first bullet under 409.2.A corrected to read: In regard to (a), this is FALSE: because the weights sum to one (i.e., alpha + beta + gamma = 1.0) and omega = long-run variance*gamma, the long-run volatility = SQRT[omega/gamma] = sqrt[omega/(1 - alpha - gamma)] = sqrt[0.0000960/(1 - 0.060 - 0.880)] = sqrt[0.0000960/0.060] = 4.0% (+1 star for @Robert...
    Per @Robert Paterson 's correction, the first bullet under 409.2.A corrected to read: In regard to (a), this is FALSE: because the weights sum to one (i.e., alpha + beta + gamma = 1.0) and omega = long-run variance*gamma, the long-run volatility = SQRT[omega/gamma] = sqrt[omega/(1 - alpha -...
    Per @Robert Paterson 's correction, the first bullet under 409.2.A corrected to read: In regard to (a), this is FALSE: because the weights sum to one (i.e., alpha + beta + gamma = 1.0) and omega...
    Replies:
    2
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    119

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