标题: Reading 9: Common Probability Distributions-LOS p 习题精选 [打印本页]
作者: 1215 时间: 2011-3-4 14:23 标题: [2011]Session 3-Reading 9: Common Probability Distributions-LOS p 习题精选
Session 3: Quantitative Methods: Application
Reading 9: Common Probability Distributions
LOS p: Explain Monte Carlo simulation and historical simulation, and describe their major applications and limitations.
A drawback of historical simulation is it:
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A) |
may not account for very rare events. | |
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B) |
depends on the accuracy of the random number generator. | |
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C) |
may not accurately reflect possible outcomes. | |
There are two major problems with historical simulation. The first is that it cannot account for events that do not occur in the sample. If a security began trading after 1987, for example, there would be no evidence of its behavior in a market crash. The other drawback is that the analyst cannot change the parameters of the distribution to examine how small changes might affect the asset’s behavior.
作者: 1215 时间: 2011-3-4 14:24
Joan Biggs, CFA, acquires a large database of past returns on a variety of assets. Biggs then draws random samples of sets of returns from the database and analyzes the resulting distributions. Biggs is engaging in:
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A) |
historical simulation. | |
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B) |
Monte Carlo simulation. | |
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This is a typical example of historical simulation.
作者: 1215 时间: 2011-3-4 14:24
Monte Carlo simulation is necessary to:
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A) |
reduce sampling error. | |
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B) |
approximate solutions to complex problems. | |
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C) |
compute continuously compounded returns. | |
This is the purpose of this type of simulation. The point is to construct distributions using complex combinations of hypothesized parameters.
作者: 1215 时间: 2011-3-4 14:24
In which of the following cases would Monte Carlo simulation least likely be needed? Payoff of a:
The probability distribution of a roulette wheel would be easy to estimate using empirical or a priori methodology.
作者: 1215 时间: 2011-3-4 14:24
Many analysts prefer to use Monte Carlo simulation rather than historical simulation because:
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A) |
past distributions cannot address changes in correlations or events that have not happened before. | |
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B) |
computers can manipulate theoretical data much more quickly than historical data. | |
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C) |
it is much easier to generate the required variables. | |
While the past is often a good predictor of the future, simulations based on past distributions are limited to reflecting changes and events that actually occurred. Monte Carlo simulation can be used to model based on parameters that are not limited to past experience.
作者: 1215 时间: 2011-3-4 14:24
The difference between a Monte Carlo simulation and a historical simulation is that a historical simulation uses randomly selected variables from past distributions, while a Monte Carlo simulation:
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A) |
projects variables based on a priori principles. | |
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B) |
uses a computer to generate random variables. | |
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C) |
uses randomly selected variables from future distributions. | |
A Monte Carlo simulation uses a computer to generate random variables from specified distributions.
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