Podcast
Questions and Answers
Which of the following statements is most accurate regarding the dataset and samples used in bootstrap resampling?
Which of the following statements is most accurate regarding the dataset and samples used in bootstrap resampling?
- A partial dataset is used, and the samples are different sizes.
- A partial dataset is used, and the samples are all the same size.
- The full dataset is used, and the samples are all the same size. (correct)
In a Monte Carlo simulation, Phillips can appropriately use:
In a Monte Carlo simulation, Phillips can appropriately use:
- only one of these variables.
- both of these variables. (correct)
- neither of these variables.
In bootstrap resampling, a single observation from a full dataset:
In bootstrap resampling, a single observation from a full dataset:
- may appear in multiple samples. (correct)
- must appear in one and only one sample.
- may appear either in exactly one sample or in no samples.
Which of the following statements describes a limitation of Monte Carlo simulation?
Which of the following statements describes a limitation of Monte Carlo simulation?
Which of the following statements regarding the distribution of returns used for asset pricing models is most accurate?
Which of the following statements regarding the distribution of returns used for asset pricing models is most accurate?
If random variable Y follows a lognormal distribution then the natural log of Y must be:
If random variable Y follows a lognormal distribution then the natural log of Y must be:
One of the major limitations of Monte Carlo simulation is that it:
One of the major limitations of Monte Carlo simulation is that it:
A lognormal distribution is least likely to be:
A lognormal distribution is least likely to be:
When resampling is done, the subsamples that are repeatedly drawn from the original observed samples will:
When resampling is done, the subsamples that are repeatedly drawn from the original observed samples will:
The goal of resampling and the use of subsamples is to estimate parameters for the:
The goal of resampling and the use of subsamples is to estimate parameters for the:
If a random variable x is lognormally distributed then ln x is:
If a random variable x is lognormally distributed then ln x is:
Monte Carlo simulation is necessary to:
Monte Carlo simulation is necessary to:
Flashcards
Bootstrap Resampling
Bootstrap Resampling
In bootstrap resampling, the samples pulled from the full dataset are all the same size. Partial datasets are not used.
Monte Carlo Input Distribution
Monte Carlo Input Distribution
Monte Carlo simulation allows analysts to specify any distribution for inputs.
Bootstrap Resampling Process
Bootstrap Resampling Process
Bootstrap resampling involves drawing repeated samples of a given size from a full dataset, replacing the sampled observations each time so that they might be redrawn in another sample.
Limitations of Monte Carlo
Limitations of Monte Carlo
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Lognormal Distribution Use
Lognormal Distribution Use
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Lognormal Distribution Natural Log
Lognormal Distribution Natural Log
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Major Limitations of Monte Carlo
Major Limitations of Monte Carlo
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Lognormal Distribution Characteristics
Lognormal Distribution Characteristics
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Resampling Sample Size
Resampling Sample Size
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Resampling Goal
Resampling Goal
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If x is lognormally distributed, then ln x is:
If x is lognormally distributed, then ln x is:
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Monte Carlo Use
Monte Carlo Use
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Study Notes
- Bootstrap resampling uses the full dataset, and the samples are the same size.
- Bootstrap resampling is based on drawing samples from a full dataset.
- Resampling replaces sampled observations for possible redrawing in another sample.
Monte Carlo Simulation
- An analyst can specify distributions for inputs in Monte Carlo simulation.
- The outcomes of a Monte Carlo simulation are only as accurate as the inputs/assumptions to the model.
- It is useful for performing "what if" scenarios
- It is fairly complex and give answers that are only as good as the assumptions used
- Does not provide insights that analytic methods can
Lognormal Distribution
- Lognormal distribution returns are used for asset pricing models.
- Lognormal distribution will not result in an asset return of less than -100%.
- The normal distribution allows for asset prices less than zero which could result in a return of less than -100% which is impossible.
- Positively skewed and bounded below by zero.
- If stock returns are continuously compounded, then prices follow a lognormal distribution under certain conditions.
- If random variable Y follows a lognormal distribution, the natural log of Y must be normally distributed.
Resampling
- In bootstrap resampling, a single observation from a full dataset may appear in multiple samples.
- When resampling, subsamples repeatedly drawn from original samples remain the same size.
- The goal of resampling and the use of subsamples is to estimate parameters for the overall population.
- It helps estimate population parameters like mean and variance.
Lognormally distributed
- If a random variable x is lognormally distributed, then ln x is normally distributed.
Monte Carlo simulation is necessary to approximate solutions to complex problems.
- The point is to construct distributions using complex combinations of hypothesized parameters.
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