P1.T2. Quantitative Analysis

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

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  1. Nicole Seaman

    P1.T2.719. One- versus two-tailed hypothesis tests (Miller Ch.7)

    Learning objectives: Differentiate between a one-tailed and a two-tailed test and identify when to use each test. Interpret the results of hypothesis tests with a specific level of confidence. Demonstrate the process of backtesting VaR by calculating the number of exceedances. Questions: 719.1. Barbara observes a sample with the following statistics: mean of X and standard deviation of Y....
    Learning objectives: Differentiate between a one-tailed and a two-tailed test and identify when to use each test. Interpret the results of hypothesis tests with a specific level of confidence. Demonstrate the process of backtesting VaR by calculating the number of exceedances. Questions: 719.1. Barbara observes a sample with the following statistics: mean of X and standard deviation of Y....
    Learning objectives: Differentiate between a one-tailed and a two-tailed test and identify when to use each test. Interpret the results of hypothesis tests with a specific level of confidence. Demonstrate the process of backtesting VaR by calculating the number of...
    Learning objectives: Differentiate between a one-tailed and a two-tailed test and identify when to use each test. Interpret the results of hypothesis tests with a specific level of confidence....
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    5
  2. Nicole Seaman

    P1.T2.718. Confidence in the mean and variance (Miller Ch.7)

    Learning objectives: Calculate and interpret the sample mean and sample variance. Construct and interpret a confidence interval. Construct an appropriate null and alternative hypothesis, and calculate an appropriate test statistic. Questions: For the following questions, please feel free to rely on the statistical lookup tables provided here: . This document contains four lookup tables (note...
    Learning objectives: Calculate and interpret the sample mean and sample variance. Construct and interpret a confidence interval. Construct an appropriate null and alternative hypothesis, and calculate an appropriate test statistic. Questions: For the following questions, please feel free to rely on the statistical lookup tables provided here: . This document contains four lookup tables (note...
    Learning objectives: Calculate and interpret the sample mean and sample variance. Construct and interpret a confidence interval. Construct an appropriate null and alternative hypothesis, and calculate an appropriate test statistic. Questions: For the following questions, please feel free to...
    Learning objectives: Calculate and interpret the sample mean and sample variance. Construct and interpret a confidence interval. Construct an appropriate null and alternative hypothesis, and...
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    0
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    5
  3. David Harper CFA FRM

    P1.T2.717. Bayes' Theorem (Miller, Ch.6)

    Learning objectives: Describe Bayes’ theorem and apply this theorem in the calculation of conditional probabilities. Compare the Bayesian approach to the frequentist approach. Apply Bayes’ theorem to scenarios with more than two possible outcomes and calculate posterior probabilities. Questions: 717.1. Your firm employs a 95.0% value at risk (VaR) model and monitors its performance by...
    Learning objectives: Describe Bayes’ theorem and apply this theorem in the calculation of conditional probabilities. Compare the Bayesian approach to the frequentist approach. Apply Bayes’ theorem to scenarios with more than two possible outcomes and calculate posterior probabilities. Questions: 717.1. Your firm employs a 95.0% value at risk (VaR) model and monitors its performance by...
    Learning objectives: Describe Bayes’ theorem and apply this theorem in the calculation of conditional probabilities. Compare the Bayesian approach to the frequentist approach. Apply Bayes’ theorem to scenarios with more than two possible outcomes and calculate posterior...
    Learning objectives: Describe Bayes’ theorem and apply this theorem in the calculation of conditional probabilities. Compare the Bayesian approach to the frequentist approach. Apply Bayes’ theorem...
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    16
  4. David Harper CFA FRM

    P1.T2.716. Central limit theore and mixture distributions (Miller, Ch 4)

    Learning objectives: Describe the central limit theorem and the implications it has when combining independent and identically distributed (i.i.d.) random variables. Describe i.i.d. random variables and the implications of the i.i.d. assumption when combining random variables. Describe a mixture distribution and explain the creation and characteristics of mixture...
    Learning objectives: Describe the central limit theorem and the implications it has when combining independent and identically distributed (i.i.d.) random variables. Describe i.i.d. random variables and the implications of the i.i.d. assumption when combining random variables. Describe a mixture distribution and explain the creation and characteristics of mixture...
    Learning objectives: Describe the central limit theorem and the implications it has when combining independent and identically distributed (i.i.d.) random variables. Describe i.i.d. random variables and the implications of the i.i.d. assumption when combining random variables. Describe a mixture...
    Learning objectives: Describe the central limit theorem and the implications it has when combining independent and identically distributed (i.i.d.) random variables. Describe i.i.d. random...
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    5
  5. Nicole Seaman

    P1.T2.715.Chi-squared distribution, Student’s t, and F-distributions (Miller Ch.4)

    Learning Objectives: Distinguish the key properties among the following distributions: ... Chi-squared distribution, Student’s t, and F-distributions. Questions: For the following questions, please rely on the statistical lookup tables provided here . This document contains four lookup tables (note that each also contains an example): cumulative standard normal distribution, student's t...
    Learning Objectives: Distinguish the key properties among the following distributions: ... Chi-squared distribution, Student’s t, and F-distributions. Questions: For the following questions, please rely on the statistical lookup tables provided here . This document contains four lookup tables (note that each also contains an example): cumulative standard normal distribution, student's t...
    Learning Objectives: Distinguish the key properties among the following distributions: ... Chi-squared distribution, Student’s t, and F-distributions. Questions: For the following questions, please rely on the statistical lookup tables provided here . This document contains four lookup tables...
    Learning Objectives: Distinguish the key properties among the following distributions: ... Chi-squared distribution, Student’s t, and F-distributions. Questions: For the following questions,...
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  6. Nicole Seaman

    P1.T2.714. Lognormal distribution (Miller Chapter 4)

    Learning objective: Distinguish the key properties among the following distributions: ... lognormal distribution Questions: 714.1. Consider a stock with an initial price of $60.00, an expected return of 9.0% per annum, and a volatility of 10.0% per annum. The continuously compounded return is assumed to be normally distributed; i.e., the log return LN[S(t)/S(0)] is approximately normal....
    Learning objective: Distinguish the key properties among the following distributions: ... lognormal distribution Questions: 714.1. Consider a stock with an initial price of $60.00, an expected return of 9.0% per annum, and a volatility of 10.0% per annum. The continuously compounded return is assumed to be normally distributed; i.e., the log return LN[S(t)/S(0)] is approximately normal....
    Learning objective: Distinguish the key properties among the following distributions: ... lognormal distribution Questions: 714.1. Consider a stock with an initial price of $60.00, an expected return of 9.0% per annum, and a volatility of 10.0% per annum. The continuously compounded return is...
    Learning objective: Distinguish the key properties among the following distributions: ... lognormal distribution Questions: 714.1. Consider a stock with an initial price of $60.00, an expected...
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  7. Nicole Seaman

    P1.T2.713. Uniform, binomial, Poisson distributions (Miller Ch.4)

    Learning Objectives: Distinguish the key properties among the following distributions: uniform distribution, Binomial distribution, Poisson distribution. Questions: 713.1. Let each A_uniform and B_uniform represent independent random uniform variables, where A_uniform falls on the interval between (0, 3) and B_uniform falls on the interval from (4, 10). Which of the following is nearest to...
    Learning Objectives: Distinguish the key properties among the following distributions: uniform distribution, Binomial distribution, Poisson distribution. Questions: 713.1. Let each A_uniform and B_uniform represent independent random uniform variables, where A_uniform falls on the interval between (0, 3) and B_uniform falls on the interval from (4, 10). Which of the following is nearest to...
    Learning Objectives: Distinguish the key properties among the following distributions: uniform distribution, Binomial distribution, Poisson distribution. Questions: 713.1. Let each A_uniform and B_uniform represent independent random uniform variables, where A_uniform falls on the interval...
    Learning Objectives: Distinguish the key properties among the following distributions: uniform distribution, Binomial distribution, Poisson distribution. Questions: 713.1. Let each A_uniform and...
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  8. Nicole Seaman

    P1.T2.712. Skew, kurtosis, coskew and cokurtosis (Miller, Chapter 3)

    Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. Describe and interpret the best linear unbiased estimator. Questions: 712.1. Consider the following discrete probability...
    Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. Describe and interpret the best linear unbiased estimator. Questions: 712.1. Consider the following discrete probability...
    Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. Describe and interpret the best...
    Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical...
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    23
  9. Nicole Seaman

    P1.T2.711. Covariance and correlation (Miller, Ch.3)

    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an unemployment outcome, U = {5, 7 or 9}. Also shown are the expected values and variances for each...
    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an unemployment outcome, U = {5, 7 or 9}. Also shown are the expected values and variances for each...
    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an...
    Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following...
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  10. Nicole Seaman

    P1.T2.710. Mean and standard deviation (Miller, Ch.3)

    Learning objectives: Interpret and apply the mean, standard deviation, and variance of a random variable. Calculate the mean, standard deviation, and variance of a discrete random variable. Interpret and calculate the expected value of a discrete random variable. Questions: 710.1. The following probability matrix contains the joint probabilities for random variables X = {2, 7, or 12} and Y =...
    Learning objectives: Interpret and apply the mean, standard deviation, and variance of a random variable. Calculate the mean, standard deviation, and variance of a discrete random variable. Interpret and calculate the expected value of a discrete random variable. Questions: 710.1. The following probability matrix contains the joint probabilities for random variables X = {2, 7, or 12} and Y =...
    Learning objectives: Interpret and apply the mean, standard deviation, and variance of a random variable. Calculate the mean, standard deviation, and variance of a discrete random variable. Interpret and calculate the expected value of a discrete random variable. Questions: 710.1. The...
    Learning objectives: Interpret and apply the mean, standard deviation, and variance of a random variable. Calculate the mean, standard deviation, and variance of a discrete random variable....
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    40
  11. Nicole Seaman

    P1.T2.709. Joint probability matrices (Miller Ch.2)

    Learning objectives: Distinguish between independent and mutually exclusive events. Define joint probability, describe a probability matrix, and calculate joint probabilities using probability matrices. Define and calculate a conditional probability, and distinguish between conditional and unconditional probabilities. Questions: 709.1. The following probability matrix gives the joint...
    Learning objectives: Distinguish between independent and mutually exclusive events. Define joint probability, describe a probability matrix, and calculate joint probabilities using probability matrices. Define and calculate a conditional probability, and distinguish between conditional and unconditional probabilities. Questions: 709.1. The following probability matrix gives the joint...
    Learning objectives: Distinguish between independent and mutually exclusive events. Define joint probability, describe a probability matrix, and calculate joint probabilities using probability matrices. Define and calculate a conditional probability, and distinguish between conditional and...
    Learning objectives: Distinguish between independent and mutually exclusive events. Define joint probability, describe a probability matrix, and calculate joint probabilities using probability...
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  12. Nicole Seaman

    P1.T2.708. Probability function fundamentals (Miller Ch. 2)

    Learning objectives: Describe and distinguish between continuous and discrete random variables. Define and distinguish between the probability density function, the cumulative distribution function, and the inverse cumulative distribution function. Calculate the probability of an event given a discrete probability function. Questions: 708.1. Let f(x) represent a probability function (which...
    Learning objectives: Describe and distinguish between continuous and discrete random variables. Define and distinguish between the probability density function, the cumulative distribution function, and the inverse cumulative distribution function. Calculate the probability of an event given a discrete probability function. Questions: 708.1. Let f(x) represent a probability function (which...
    Learning objectives: Describe and distinguish between continuous and discrete random variables. Define and distinguish between the probability density function, the cumulative distribution function, and the inverse cumulative distribution function. Calculate the probability of an event given a...
    Learning objectives: Describe and distinguish between continuous and discrete random variables. Define and distinguish between the probability density function, the cumulative distribution...
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  13. Nicole Seaman

    P1.T2.707. Gaussian Copula (Hull)

    Learning objectives: Define copula and describe the key properties of copulas and copula correlation. Explain tail dependence. Describe the Gaussian copula, Student’s t-copula, multivariate copula, and one-factor copula. Questions: 707.1. Below are the joint probabilities for a cumulative bivariate normal distribution with a correlation parameter, ρ, of 0.30. If V(1) and V(2) are each...
    Learning objectives: Define copula and describe the key properties of copulas and copula correlation. Explain tail dependence. Describe the Gaussian copula, Student’s t-copula, multivariate copula, and one-factor copula. Questions: 707.1. Below are the joint probabilities for a cumulative bivariate normal distribution with a correlation parameter, ρ, of 0.30. If V(1) and V(2) are each...
    Learning objectives: Define copula and describe the key properties of copulas and copula correlation. Explain tail dependence. Describe the Gaussian copula, Student’s t-copula, multivariate copula, and one-factor copula. Questions: 707.1. Below are the joint probabilities for a cumulative...
    Learning objectives: Define copula and describe the key properties of copulas and copula correlation. Explain tail dependence. Describe the Gaussian copula, Student’s t-copula, multivariate...
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  14. Nicole Seaman

    P1.T2.706. Bivariate normal distribution (Hull)

    @David Harper CFA FRM, makes perfect sense now. thanks for taking the time again.
    @David Harper CFA FRM, makes perfect sense now. thanks for taking the time again.
    @David Harper CFA FRM, makes perfect sense now. thanks for taking the time again.
    @David Harper CFA FRM, makes perfect sense now. thanks for taking the time again.
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  15. Nicole Seaman

    P1.T2.705. Correlation (Hull)

    Thank you emilioalzamora and David for such a detailed explanation.
    Thank you emilioalzamora and David for such a detailed explanation.
    Thank you emilioalzamora and David for such a detailed explanation.
    Thank you emilioalzamora and David for such a detailed explanation.
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  16. Nicole Seaman

    P1.T2.704. Forecasting volatility with GARCH (Hull)

    Thanks - much appreciate the extra detail David. I knew that omega = weight * long-run variance. What was confusing me generally was knowing from the question (whether variance or covariance/ correlation omega), when to do the omega -> variance-only conversion i.e. when to leave omega as is or split it up
    Thanks - much appreciate the extra detail David. I knew that omega = weight * long-run variance. What was confusing me generally was knowing from the question (whether variance or covariance/ correlation omega), when to do the omega -> variance-only conversion i.e. when to leave omega as is or split it up
    Thanks - much appreciate the extra detail David. I knew that omega = weight * long-run variance. What was confusing me generally was knowing from the question (whether variance or covariance/ correlation omega), when to do the omega -> variance-only conversion i.e. when to leave omega as is or...
    Thanks - much appreciate the extra detail David. I knew that omega = weight * long-run variance. What was confusing me generally was knowing from the question (whether variance or covariance/...
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    6
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    90
  17. Nicole Seaman

    P1.T2.703. EWMA versus GARCH volatility (Hull)

    Hi [USER=48426]@ That's really just an elaboration on Hull's End of Chapter Question 11.6, and I can't see any problem with it, although it's highly tedious! :eek: I entered into into the more generic version (in our learning XLS), see here (and below) at It's true that there are two omega (ω) assumptions given but that looks okay (consistent) with Hull. Please note that GARCH is used twice...
    Hi [USER=48426]@ That's really just an elaboration on Hull's End of Chapter Question 11.6, and I can't see any problem with it, although it's highly tedious! :eek: I entered into into the more generic version (in our learning XLS), see here (and below) at It's true that there are two omega (ω) assumptions given but that looks okay (consistent) with Hull. Please note that GARCH is used twice...
    Hi [USER=48426]@ That's really just an elaboration on Hull's End of Chapter Question 11.6, and I can't see any problem with it, although it's highly tedious! :eek: I entered into into the more generic version (in our learning XLS), see here (and below) at It's true that there are two omega (ω)...
    Hi [USER=48426]@ That's really just an elaboration on Hull's End of Chapter Question 11.6, and I can't see any problem with it, although it's highly tedious! :eek: I entered into into the more...
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  18. Nicole Seaman

    P1.T2.699. Linear and nonlinear trends (Diebold)

    Hi @Buglia Yes, you are totally correct, it's my mistake. Thank you! I added that phrase myself, to Diebold's source question, to help but it should read what you say: "... where y(0) is 1990:Q1". What you did makes sense. @Nicole Seaman I just edited question 699.2 with a minor text change (emphasis is the change):
    Hi @Buglia Yes, you are totally correct, it's my mistake. Thank you! I added that phrase myself, to Diebold's source question, to help but it should read what you say: "... where y(0) is 1990:Q1". What you did makes sense. @Nicole Seaman I just edited question 699.2 with a minor text change (emphasis is the change):
    Hi @Buglia Yes, you are totally correct, it's my mistake. Thank you! I added that phrase myself, to Diebold's source question, to help but it should read what you say: "... where y(0) is 1990:Q1". What you did makes sense. @Nicole Seaman I just edited question 699.2 with a minor text change...
    Hi @Buglia Yes, you are totally correct, it's my mistake. Thank you! I added that phrase myself, to Diebold's source question, to help but it should read what you say: "... where y(0) is...
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  19. Nicole Seaman

    P1.T2.702. Simple (equally weighted) historical volatility (Hull)

    Learning objectives: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Questions 702.1. Consider the following series of closing stock prices over the tend most recent trading day (this is similar to Hull's Table 10.3) along with daily log returns, squared...
    Learning objectives: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Questions 702.1. Consider the following series of closing stock prices over the tend most recent trading day (this is similar to Hull's Table 10.3) along with daily log returns, squared...
    Learning objectives: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating volatility. Questions 702.1. Consider the following series of closing stock prices over the tend most...
    Learning objectives: Define and distinguish between volatility, variance rate, and implied volatility. Describe the power law. Explain how various weighting schemes can be used in estimating...
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    34
  20. Nicole Seaman

    P1.T2.701. Regression analysis to model seasonality (Diebold)

    Many thanks for you lovely comment, Brian. It is nothing special I guess, I have just read a few textbooks that's it. The modelling (implementation work) is another kettle of fish. These "goodness of fit" tests are technically quite complex. Again, thanks for your like!
    Many thanks for you lovely comment, Brian. It is nothing special I guess, I have just read a few textbooks that's it. The modelling (implementation work) is another kettle of fish. These "goodness of fit" tests are technically quite complex. Again, thanks for your like!
    Many thanks for you lovely comment, Brian. It is nothing special I guess, I have just read a few textbooks that's it. The modelling (implementation work) is another kettle of fish. These "goodness of fit" tests are technically quite complex. Again, thanks for your like!
    Many thanks for you lovely comment, Brian. It is nothing special I guess, I have just read a few textbooks that's it. The modelling (implementation work) is another kettle of fish. These "goodness...
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    11
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    104
  21. Nicole Seaman

    P1.T2.700. Seasonality in time series analysis (Diebold)

    Learning objective: Describe the sources of seasonality and how to deal with it in time series analysis. Questions 700.1. Which of the following time series is MOST LIKELY to contain a seasonal pattern? a. Price of solar panels b. Employment participation rate c. Climate data data recorded from a weather station once per year d. Return on average assets (ROA) for the large commercial bank...
    Learning objective: Describe the sources of seasonality and how to deal with it in time series analysis. Questions 700.1. Which of the following time series is MOST LIKELY to contain a seasonal pattern? a. Price of solar panels b. Employment participation rate c. Climate data data recorded from a weather station once per year d. Return on average assets (ROA) for the large commercial bank...
    Learning objective: Describe the sources of seasonality and how to deal with it in time series analysis. Questions 700.1. Which of the following time series is MOST LIKELY to contain a seasonal pattern? a. Price of solar panels b. Employment participation rate c. Climate data data recorded...
    Learning objective: Describe the sources of seasonality and how to deal with it in time series analysis. Questions 700.1. Which of the following time series is MOST LIKELY to contain a seasonal...
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  22. Nicole Seaman

    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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  23. Nicole Seaman

    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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  24. Nicole Seaman

    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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  25. 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...
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  26. David Harper CFA FRM

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

    [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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  27. Nicole Seaman

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

    In the study notes it is explained that "If the inverses of all roots of Θ(L) are inside the unit circle, then the process is invertible". Pg 42 of quantitative analysis - Diebold, Elements of Forecasting, Chapters 5, 6 7 & 8
    In the study notes it is explained that "If the inverses of all roots of Θ(L) are inside the unit circle, then the process is invertible". Pg 42 of quantitative analysis - Diebold, Elements of Forecasting, Chapters 5, 6 7 & 8
    In the study notes it is explained that "If the inverses of all roots of Θ(L) are inside the unit circle, then the process is invertible". Pg 42 of quantitative analysis - Diebold, Elements of Forecasting, Chapters 5, 6 7 & 8
    In the study notes it is explained that "If the inverses of all roots of Θ(L) are inside the unit circle, then the process is invertible". Pg 42 of quantitative analysis - Diebold, Elements of...
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    231
  28. 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:
    171
  29. Nicole Seaman

    P1.T2.508. Wold's theorem (Diebold)

    [USER=42750]@ Okay great. No worries, honestly I learn something new almost every time that I take a fresh look at something! Good luck with your studies ...
    [USER=42750]@ Okay great. No worries, honestly I learn something new almost every time that I take a fresh look at something! Good luck with your studies ...
    [USER=42750]@ Okay great. No worries, honestly I learn something new almost every time that I take a fresh look at something! Good luck with your studies ...
    [USER=42750]@ Okay great. No worries, honestly I learn something new almost every time that I take a fresh look at something! Good luck with your studies ...
    Replies:
    4
    Views:
    266
  30. 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
    Views:
    142

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