P1.T2. Quantitative Analysis

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

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  1. Nicole Seaman

    PQ-T2 P1.T2.320. Statistical inference: hypothesis testing and confidence intervals (topic review)

    Thank you Jayanthi.......... Helps a lot
    Thank you Jayanthi.......... Helps a lot
    Thank you Jayanthi.......... Helps a lot
    Thank you Jayanthi.......... Helps a lot
    Replies:
    5
    Views:
    357
  2. Suzanne Evans

    P1.T2.205 Sampling distributions (Stock & Watson)

    Thanks, David. I agree that your questions go deeper than the notes, which is definitely great for gaining a deep understanding. I'll be honest, I got a little frustrated as I had only gone up to chapters 2 and 3 (per your notes) which map to chapter 1 and 2 in the GARP ebooks (I'm not a big fan of Pearson etext); I thought I was missing some concepts. Going forward, I'll make sure I've...
    Thanks, David. I agree that your questions go deeper than the notes, which is definitely great for gaining a deep understanding. I'll be honest, I got a little frustrated as I had only gone up to chapters 2 and 3 (per your notes) which map to chapter 1 and 2 in the GARP ebooks (I'm not a big fan of Pearson etext); I thought I was missing some concepts. Going forward, I'll make sure I've...
    Thanks, David. I agree that your questions go deeper than the notes, which is definitely great for gaining a deep understanding. I'll be honest, I got a little frustrated as I had only gone up to chapters 2 and 3 (per your notes) which map to chapter 1 and 2 in the GARP ebooks (I'm not a big...
    Thanks, David. I agree that your questions go deeper than the notes, which is definitely great for gaining a deep understanding. I'll be honest, I got a little frustrated as I had only gone up...
    Replies:
    9
    Views:
    326
  3. Suzanne Evans

    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:
    402
  4. David Harper CFA FRM

    L1.T2.107 GARCH/EWMA maximum likelihood method (MLE) (Hull)

    Hi @superpocoyo (Melody) I agree, I just did a keyword search of the AIM syllabus and it appears that MLE has been dropped. Thanks,
    Hi @superpocoyo (Melody) I agree, I just did a keyword search of the AIM syllabus and it appears that MLE has been dropped. Thanks,
    Hi @superpocoyo (Melody) I agree, I just did a keyword search of the AIM syllabus and it appears that MLE has been dropped. Thanks,
    Hi @superpocoyo (Melody) I agree, I just did a keyword search of the AIM syllabus and it appears that MLE has been dropped. Thanks,
    Replies:
    6
    Views:
    195
  5. Nicole Seaman

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

    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...
    Replies:
    0
    Views:
    90
  6. Nicole Seaman

    P1.T2.509. Box-Pierce and Ljung-Box Q-statistics (Diebold)

    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
    Views:
    214
  7. Suzanne Evans

    P1.T2.218. Theory of Ordinary Least Squares (Stock & Watson)

    In reference to homoskedastic: sometimes it is mentioned "variance constant" and other times "mean zero"... "The error term u(i) is homoskedastic if the variance of the conditional distribution of u(i) given X(i) is constant for i = 1,…,n and in particular does not depend on X(i)." Is both mean the same thing?
    In reference to homoskedastic: sometimes it is mentioned "variance constant" and other times "mean zero"... "The error term u(i) is homoskedastic if the variance of the conditional distribution of u(i) given X(i) is constant for i = 1,…,n and in particular does not depend on X(i)." Is both mean the same thing?
    In reference to homoskedastic: sometimes it is mentioned "variance constant" and other times "mean zero"... "The error term u(i) is homoskedastic if the variance of the conditional distribution of u(i) given X(i) is constant for i = 1,…,n and in particular does not depend on X(i)." Is both...
    In reference to homoskedastic: sometimes it is mentioned "variance constant" and other times "mean zero"... "The error term u(i) is homoskedastic if the variance of the conditional distribution...
    Replies:
    4
    Views:
    205
  8. David Harper CFA FRM

    L1.T2.74 F-distribution (Gujarati)

    Not to worry - they will give you the lookup table for the F distribution, if they do:) Thanks! Jayanthi
    Not to worry - they will give you the lookup table for the F distribution, if they do:) Thanks! Jayanthi
    Not to worry - they will give you the lookup table for the F distribution, if they do:) Thanks! Jayanthi
    Not to worry - they will give you the lookup table for the F distribution, if they do:) Thanks! Jayanthi
    Replies:
    18
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    174
  9. Nicole Seaman

    P1.T2.507. White noise (Diebold)

    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...
    Replies:
    0
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    163
  10. Suzanne Evans

    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)
    Replies:
    24
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    678
  11. David Harper CFA FRM

    L1.T2.105 Generalized auto regressive conditional heteroscedasticity, GARCH(p,q) (Hull)

    Thanks you David for taking out time to answer. That clears my doubt. Have a nice evening.
    Thanks you David for taking out time to answer. That clears my doubt. Have a nice evening.
    Thanks you David for taking out time to answer. That clears my doubt. Have a nice evening.
    Thanks you David for taking out time to answer. That clears my doubt. Have a nice evening.
    Replies:
    8
    Views:
    205
  12. Suzanne Evans

    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:
    461
  13. Nicole Seaman

    Quiz-T2 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
    Views:
    183
  14. David Harper CFA FRM

    L1.T2.128 Simulation with inverse transform method (Jorion)

    Hi @Tipo GBM doesn't contain the deviate, GBM models the asset price: price change = drift*Δt + sigma* epsilon* sqrt(Δt). Dowd's market risk VaR = -drift + sigma*deviate precisely because the +drift is positive in GBM. Say drift is 10% and sigma is 30%. We can input those into GBM to model the asset price. But how is risk measures (VaR)? It's a loss which is mitigated by the drift. So...
    Hi @Tipo GBM doesn't contain the deviate, GBM models the asset price: price change = drift*Δt + sigma* epsilon* sqrt(Δt). Dowd's market risk VaR = -drift + sigma*deviate precisely because the +drift is positive in GBM. Say drift is 10% and sigma is 30%. We can input those into GBM to model the asset price. But how is risk measures (VaR)? It's a loss which is mitigated by the drift. So...
    Hi @Tipo GBM doesn't contain the deviate, GBM models the asset price: price change = drift*Δt + sigma* epsilon* sqrt(Δt). Dowd's market risk VaR = -drift + sigma*deviate precisely because the +drift is positive in GBM. Say drift is 10% and sigma is 30%. We can input those into GBM to model...
    Hi @Tipo GBM doesn't contain the deviate, GBM models the asset price: price change = drift*Δt + sigma* epsilon* sqrt(Δt). Dowd's market risk VaR = -drift + sigma*deviate precisely because the...
    Replies:
    6
    Views:
    87
  15. David Harper CFA FRM

    L1.T2.57 Methodology of Econometrics (Gujarati)

    Hi @tosuhn this are aged questions (from Gujarati's econometrics which is no longer assigned) so most of this won't appear on the exam. Thanks,
    Hi @tosuhn this are aged questions (from Gujarati's econometrics which is no longer assigned) so most of this won't appear on the exam. Thanks,
    Hi @tosuhn this are aged questions (from Gujarati's econometrics which is no longer assigned) so most of this won't appear on the exam. Thanks,
    Hi @tosuhn this are aged questions (from Gujarati's econometrics which is no longer assigned) so most of this won't appear on the exam. Thanks,
    Replies:
    4
    Views:
    78
  16. David Harper CFA FRM

    L1.T2.101 Monte Carlo simulation accuracy (Jorion)

    Hi @Tipo I am not seeing the exact Miller reference but that does sound correct for the CI of a sample mean; i.e., CI[sample mean] = sample mean +/- (critical t)*(standard error; i.e., standard deviation of the sampling distribution). But this question concerns the confidence interval around the VaR quantile (not a sample mean): "the confidence interval for the VaR quantile is [1.245,2.045]."...
    Hi @Tipo I am not seeing the exact Miller reference but that does sound correct for the CI of a sample mean; i.e., CI[sample mean] = sample mean +/- (critical t)*(standard error; i.e., standard deviation of the sampling distribution). But this question concerns the confidence interval around the VaR quantile (not a sample mean): "the confidence interval for the VaR quantile is [1.245,2.045]."...
    Hi @Tipo I am not seeing the exact Miller reference but that does sound correct for the CI of a sample mean; i.e., CI[sample mean] = sample mean +/- (critical t)*(standard error; i.e., standard deviation of the sampling distribution). But this question concerns the confidence interval around the...
    Hi @Tipo I am not seeing the exact Miller reference but that does sound correct for the CI of a sample mean; i.e., CI[sample mean] = sample mean +/- (critical t)*(standard error; i.e., standard...
    Replies:
    16
    Views:
    199
  17. David Harper CFA FRM

    L1.T2.96 Multivariate regression estimates (Gujarati)

    Hi @Aenny In regressions, the above is the most typical application (it firstly depends on how the null is defined, but when it's typical, as above, no explication is needed): the null hypothesis is that the partial slope coefficient is equal to zero; i.e., the null says there is no relationship. (A one-sided test adds the less than or greater than, < or >, but a key fact about the null is...
    Hi @Aenny In regressions, the above is the most typical application (it firstly depends on how the null is defined, but when it's typical, as above, no explication is needed): the null hypothesis is that the partial slope coefficient is equal to zero; i.e., the null says there is no relationship. (A one-sided test adds the less than or greater than, < or >, but a key fact about the null is...
    Hi @Aenny In regressions, the above is the most typical application (it firstly depends on how the null is defined, but when it's typical, as above, no explication is needed): the null hypothesis is that the partial slope coefficient is equal to zero; i.e., the null says there is no...
    Hi @Aenny In regressions, the above is the most typical application (it firstly depends on how the null is defined, but when it's typical, as above, no explication is needed): the null...
    Replies:
    11
    Views:
    142
  18. David Harper CFA FRM

    L1.T2.125. Generalized Pareto distribution (GPD) (Rachov)

    Hi Aenny, I think you should recalculate the part "(1+ 0.213333)^(-8.333)". Here I get ~0.1996 which can be approximated to 0.20 and so 1-0.2 = 0.8.
    Hi Aenny, I think you should recalculate the part "(1+ 0.213333)^(-8.333)". Here I get ~0.1996 which can be approximated to 0.20 and so 1-0.2 = 0.8.
    Hi Aenny, I think you should recalculate the part "(1+ 0.213333)^(-8.333)". Here I get ~0.1996 which can be approximated to 0.20 and so 1-0.2 = 0.8.
    Hi Aenny, I think you should recalculate the part "(1+ 0.213333)^(-8.333)". Here I get ~0.1996 which can be approximated to 0.20 and so 1-0.2 = 0.8.
    Replies:
    4
    Views:
    81
  19. David Harper CFA FRM

    L1.T2.60 Bayes Theorem (Gujarati)

    Thank you David. That made it clear.
    Thank you David. That made it clear.
    Thank you David. That made it clear.
    Thank you David. That made it clear.
    Replies:
    5
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    101
  20. David Harper CFA FRM

    L1.T2.63 Chebyshev’s Inequality (Gujarati)

    Hi Aenny, when you calculate tests for VaR you should take the one-tailed test because VaR per its definition looks at the left-tail of a distribution (i.e. the maximum losses).
    Hi Aenny, when you calculate tests for VaR you should take the one-tailed test because VaR per its definition looks at the left-tail of a distribution (i.e. the maximum losses).
    Hi Aenny, when you calculate tests for VaR you should take the one-tailed test because VaR per its definition looks at the left-tail of a distribution (i.e. the maximum losses).
    Hi Aenny, when you calculate tests for VaR you should take the one-tailed test because VaR per its definition looks at the left-tail of a distribution (i.e. the maximum losses).
    Replies:
    15
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    73
  21. David Harper CFA FRM

    L1.T2.117 Chi-square distribution (Rachev)

    Thanks, David, that helped! :) Especially this part: "For me personally, this is the hardest part to see: squaring the "two-sided" normal variable which is symmetrical around the zero (so includes negatives) informs a "one-sided" chi-square variable; in short, if (Z<-1.645 or Z>1.645), then abs|z|>1.645 and z^2>1.645^2" This was the part which went beyond my understanding at that...
    Thanks, David, that helped! :) Especially this part: "For me personally, this is the hardest part to see: squaring the "two-sided" normal variable which is symmetrical around the zero (so includes negatives) informs a "one-sided" chi-square variable; in short, if (Z<-1.645 or Z>1.645), then abs|z|>1.645 and z^2>1.645^2" This was the part which went beyond my understanding at that...
    Thanks, David, that helped! :) Especially this part: "For me personally, this is the hardest part to see: squaring the "two-sided" normal variable which is symmetrical around the zero (so includes negatives) informs a "one-sided" chi-square variable; in short, if (Z<-1.645 or Z>1.645), then...
    Thanks, David, that helped! :) Especially this part: "For me personally, this is the hardest part to see: squaring the "two-sided" normal variable which is symmetrical around the zero (so includes...
    Replies:
    8
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    181
  22. David Harper CFA FRM

    L1.T2.95 Multivariate regression (Gujarati)

    Hi 95.3.B might better say "and [many | several | most] partial slope coefficients" are insignificant. A classic near-perfect multicollinearity is one (or two) high t-stat for one coefficient (i.e., significance of one partial) and low t-stats for all of the others (insignificant) with ~ perfect MC ensuring a high F ratio. The key thing is that the F ratio is a test of a joint null hypothesis...
    Hi 95.3.B might better say "and [many | several | most] partial slope coefficients" are insignificant. A classic near-perfect multicollinearity is one (or two) high t-stat for one coefficient (i.e., significance of one partial) and low t-stats for all of the others (insignificant) with ~ perfect MC ensuring a high F ratio. The key thing is that the F ratio is a test of a joint null hypothesis...
    Hi 95.3.B might better say "and [many | several | most] partial slope coefficients" are insignificant. A classic near-perfect multicollinearity is one (or two) high t-stat for one coefficient (i.e., significance of one partial) and low t-stats for all of the others (insignificant) with ~...
    Hi 95.3.B might better say "and [many | several | most] partial slope coefficients" are insignificant. A classic near-perfect multicollinearity is one (or two) high t-stat for one coefficient...
    Replies:
    2
    Views:
    61
  23. David Harper CFA FRM

    L1.T2.84 Stochastic error term (Gujarati)

    Thanks David!
    Thanks David!
    Thanks David!
    Thanks David!
    Replies:
    3
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    59
  24. Nicole Seaman

    P1.T2.402. Random number generators (Fabozzi)

    AIMs: Describe the inverse transform method and its implementation in discrete and continuous distributions. Describe standards for an effective pseudorandom number generator and explain midsquare technique and congruential pseudorandom number generators. Describe quasi-random (low-discrepancy) sequences and explain how they work in simulations. Explain the mechanics and characteristics of the...
    AIMs: Describe the inverse transform method and its implementation in discrete and continuous distributions. Describe standards for an effective pseudorandom number generator and explain midsquare technique and congruential pseudorandom number generators. Describe quasi-random (low-discrepancy) sequences and explain how they work in simulations. Explain the mechanics and characteristics of the...
    AIMs: Describe the inverse transform method and its implementation in discrete and continuous distributions. Describe standards for an effective pseudorandom number generator and explain midsquare technique and congruential pseudorandom number generators. Describe quasi-random (low-discrepancy)...
    AIMs: Describe the inverse transform method and its implementation in discrete and continuous distributions. Describe standards for an effective pseudorandom number generator and explain midsquare...
    Replies:
    0
    Views:
    104
  25. David Harper CFA FRM

    L1.T2.119 Lognormal distribution (Rachev)

    Many thanks for the thorough response David (and apologies as on second review my question seems a little basic!).
    Many thanks for the thorough response David (and apologies as on second review my question seems a little basic!).
    Many thanks for the thorough response David (and apologies as on second review my question seems a little basic!).
    Many thanks for the thorough response David (and apologies as on second review my question seems a little basic!).
    Replies:
    6
    Views:
    154
  26. David Harper CFA FRM

    L1.T2.88 Linear regression assumptions (Gujarati)

    Hi wanderer, I don't have an easy intuition to it myself. It appears to be a feature of an OLS regression that the sum of the product of the residuals and the explanatory (i.e., independent) variables is necessarily zero. The mean of the errors must be zero, so I fear I am missing some intuition related to the cross-product necessarily being zero, but I just can't get the intuition myself. ...
    Hi wanderer, I don't have an easy intuition to it myself. It appears to be a feature of an OLS regression that the sum of the product of the residuals and the explanatory (i.e., independent) variables is necessarily zero. The mean of the errors must be zero, so I fear I am missing some intuition related to the cross-product necessarily being zero, but I just can't get the intuition myself. ...
    Hi wanderer, I don't have an easy intuition to it myself. It appears to be a feature of an OLS regression that the sum of the product of the residuals and the explanatory (i.e., independent) variables is necessarily zero. The mean of the errors must be zero, so I fear I am missing some...
    Hi wanderer, I don't have an easy intuition to it myself. It appears to be a feature of an OLS regression that the sum of the product of the residuals and the explanatory (i.e., independent)...
    Replies:
    5
    Views:
    81
  27. David Harper CFA FRM

    L1.T2.133 Cholesky factorization (Jorion)

    Hi bball8530 - I so wish i could say (I have for years asked GARP to settle on a formula sheet). This one is "on the fence" in my opinion: it is fundamental and it has, I think, fully three (3) occurrences in 2013 P1 (including new Miller). On the other hand, I don't recall any feedback instance that has required its memorization and further, notice this AIM is: Explain how to simulate...
    Hi bball8530 - I so wish i could say (I have for years asked GARP to settle on a formula sheet). This one is "on the fence" in my opinion: it is fundamental and it has, I think, fully three (3) occurrences in 2013 P1 (including new Miller). On the other hand, I don't recall any feedback instance that has required its memorization and further, notice this AIM is: Explain how to simulate...
    Hi bball8530 - I so wish i could say (I have for years asked GARP to settle on a formula sheet). This one is "on the fence" in my opinion: it is fundamental and it has, I think, fully three (3) occurrences in 2013 P1 (including new Miller). On the other hand, I don't recall any feedback instance...
    Hi bball8530 - I so wish i could say (I have for years asked GARP to settle on a formula sheet). This one is "on the fence" in my opinion: it is fundamental and it has, I think, fully three (3)...
    Replies:
    9
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    117
  28. David Harper CFA FRM

    L1.T2.97 Geometric Brownian motion (GBM) Monte Carlo simulation (Jorion)

    Hi Showstopper, With respect to MCS, the user can decide on any process, but in the case of the typical GBM, which includes a drift, I suppose a possible reason to ignore the drift is simply when the assumption is wanted that the drift = 0; i.e., it exists but is simply assumed zero. Specifically, if the time window is short (e.g., 10 days) such that the expected 10-day return is near enough...
    Hi Showstopper, With respect to MCS, the user can decide on any process, but in the case of the typical GBM, which includes a drift, I suppose a possible reason to ignore the drift is simply when the assumption is wanted that the drift = 0; i.e., it exists but is simply assumed zero. Specifically, if the time window is short (e.g., 10 days) such that the expected 10-day return is near enough...
    Hi Showstopper, With respect to MCS, the user can decide on any process, but in the case of the typical GBM, which includes a drift, I suppose a possible reason to ignore the drift is simply when the assumption is wanted that the drift = 0; i.e., it exists but is simply assumed zero....
    Hi Showstopper, With respect to MCS, the user can decide on any process, but in the case of the typical GBM, which includes a drift, I suppose a possible reason to ignore the drift is simply...
    Replies:
    13
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    207
  29. David Harper CFA FRM

    L1.T2.106 GARCH(1,1) mean reversion (Hull)

    Hi Joe, only because the question asks "What is implied long-run volatility?" not long run (unconditional) variance. I agree with you that the implied LR variance = 0.0002/ (1-0.90-0.5). Hope that clarifies, thanks!
    Hi Joe, only because the question asks "What is implied long-run volatility?" not long run (unconditional) variance. I agree with you that the implied LR variance = 0.0002/ (1-0.90-0.5). Hope that clarifies, thanks!
    Hi Joe, only because the question asks "What is implied long-run volatility?" not long run (unconditional) variance. I agree with you that the implied LR variance = 0.0002/ (1-0.90-0.5). Hope that clarifies, thanks!
    Hi Joe, only because the question asks "What is implied long-run volatility?" not long run (unconditional) variance. I agree with you that the implied LR variance = 0.0002/ (1-0.90-0.5). Hope that...
    Replies:
    4
    Views:
    99
  30. David Harper CFA FRM

    L1.T2.100 Option simulations (MCS) (Jorion)

    Hi arnanpices, The GBM, which is common for equities, models the change in stock price as: upward drift + random shock (mean = 0). Note this implies that we expect the stock (asset price) to increase over time. (although we may expect the stock volatility/variance to revert!) But we do not expect interest rates to increase forever over time. So, the CIR model would be more appropriate when...
    Hi arnanpices, The GBM, which is common for equities, models the change in stock price as: upward drift + random shock (mean = 0). Note this implies that we expect the stock (asset price) to increase over time. (although we may expect the stock volatility/variance to revert!) But we do not expect interest rates to increase forever over time. So, the CIR model would be more appropriate when...
    Hi arnanpices, The GBM, which is common for equities, models the change in stock price as: upward drift + random shock (mean = 0). Note this implies that we expect the stock (asset price) to increase over time. (although we may expect the stock volatility/variance to revert!) But we do not...
    Hi arnanpices, The GBM, which is common for equities, models the change in stock price as: upward drift + random shock (mean = 0). Note this implies that we expect the stock (asset price) to...
    Replies:
    4
    Views:
    94

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