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    Reading 6 Simulation Methods

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    Nghia Tuan Nghia
    The document contains 12 multiple choice questions related to financial modeling techniques like bootstrap resampling, Monte Carlo simulation, and distributions like normal and lognormal. The questions cover topics like the characteristics of samples used in bootstrap resampling, how variables can be modeled in Monte Carlo simulation, limitations of Monte Carlo, appropriate distributions to use for asset pricing models, properties of lognormal distributions, and the goals of resampling and Monte Carlo simulation.

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    Reading 6 Simulation Methods

    Uploaded by

    Nghia Tuan Nghia
    24 views4 pages
    The document contains 12 multiple choice questions related to financial modeling techniques like bootstrap resampling, Monte Carlo simulation, and distributions like normal and lognormal. The questions cover topics like the characteristics of samples used in bootstrap resampling, how variables can be modeled in Monte Carlo simulation, limitations of Monte Carlo, appropriate distributions to use for asset pricing models, properties of lognormal distributions, and the goals of resampling and Monte Carlo simulation.

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    The document contains 12 multiple choice questions related to financial modeling techniques like bootstrap resampling, Monte Carlo simulation, and distributions like normal and lognormal. The questions cover topics like the characteristics of samples used in bootstrap resampling, how variables can be modeled in Monte Carlo simulation, limitations of Monte Carlo, appropriate distributions to use for asset pricing models, properties of lognormal distributions, and the goals of resampling and Monte Carlo simulation.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    24 views4 pages

    Reading 6 Simulation Methods

    Uploaded by

    Nghia Tuan Nghia
    The document contains 12 multiple choice questions related to financial modeling techniques like bootstrap resampling, Monte Carlo simulation, and distributions like normal and lognormal. The questions cover topics like the characteristics of samples used in bootstrap resampling, how variables can be modeled in Monte Carlo simulation, limitations of Monte Carlo, appropriate distributions to use for asset pricing models, properties of lognormal distributions, and the goals of resampling and Monte Carlo simulation.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 4

    Question #1 of 12 Question ID: 1572830

    Which of the following statements is most accurate regarding the dataset and samples used
    in bootstrap resampling?

    A) A partial dataset is used, and the samples are different sizes.


    B) The full dataset is used, and the samples are all the same size.
    C) A partial dataset is used, and the samples are all the same size.

    Question #2 of 12 Question ID: 1572825

    Bill Phillips is developing a Monte Carlo simulation to value a complex and thinly traded
    security. Phillips wants to model one input variable to have negative skewness and a second
    input variable to have positive excess kurtosis. In a Monte Carlo simulation, Phillips can
    appropriately use:

    A) neither of these variables.


    B) both of these variables.
    C) only one of these variables.

    Question #3 of 12 Question ID: 1572827

    In bootstrap resampling, a single observation from a full dataset:

    A) may appear in multiple samples.


    B) may appear either in exactly one sample or in no samples.
    C) must appear in one and only one sample.

    Question #4 of 12 Question ID: 1572823


    Which of the following statements describes a limitation of Monte Carlo simulation?

    A) Outcomes of a simulation can only be as accurate as the inputs to the model.


    Simulations do not consider possible input values that lie outside historical
    B)
    experience.
    Variables are assumed to be normally distributed but may actually have non-normal
    C)
    distributions.

    Question #5 of 12 Question ID: 1572821

    Which of the following statements regarding the distribution of returns used for asset
    pricing models is most accurate?

    Lognormal distribution returns are used because this will allow for negative returns
    A)
    on the assets.
    Normal distribution returns are used for asset pricing models because they will only
    B)
    allow the asset price to fall to zero.
    Lognormal distribution returns are used for asset pricing models because they will
    C)
    not result in an asset return of less than -100%.

    Question #6 of 12 Question ID: 1572819

    If random variable Y follows a lognormal distribution then the natural log of Y must be:

    A) denoted as ex.

    B) normally distributed.
    C) lognormally distributed.

    Question #7 of 12 Question ID: 1572826

    One of the major limitations of Monte Carlo simulation is that it:

    A) cannot provide the insight that analytic methods can.


    B) does not lend itself to performing “what if” scenarios.
    C) requires that variables be modeled using the normal distribution.

    Question #8 of 12 Question ID: 1572822

    A lognormal distribution is least likely to be:

    A) bounded below by zero.


    B) used to model stock prices.
    C) negatively skewed.

    Question #9 of 12 Question ID: 1572828

    When resampling is done, the subsamples that are repeatedly drawn from the original
    observed samples will:

    A) progressively get larger.


    B) progressively get smaller.
    C) remain the same size.

    Question #10 of 12 Question ID: 1572829

    The goal of resampling and the use of subsamples is to estimate parameters for the:

    A) various subsamples.
    B) overall population.
    C) original sample.

    Question #11 of 12 Question ID: 1572820


    If a random variable x is lognormally distributed then ln x is:

    A) abnormally distributed.

    B) defined as ex.

    C) normally distributed.

    Question #12 of 12 Question ID: 1572824

    Monte Carlo simulation is necessary to:

    A) reduce sampling error.


    B) compute continuously compounded returns.
    C) approximate solutions to complex problems.

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