Reading 9: Common Probability Distributions-LOS n 习题精选 normal, between, center, 2011

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Session 3: Quantitative Methods: Application
Reading 9: Common Probability Distributions

LOS n: Explain the relationship between normal and lognormal distributions and why the lognormal distribution is used to model asset prices.

 

 

The farthest point on the left side of the lognormal distribution:

A) is skewed to the left.

B) can be any negative number.

C) is bounded by 0.

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The lognormal distribution is skewed to the right with a long right hand tail and is bounded on the left hand side of the curve by zero.

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

A) Normal distribution returns are used for asset pricing models because they will only allow the asset price to fall to zero.

B) Lognormal distribution returns are used because this will allow for negative returns on the assets.

C) Lognormal distribution returns are used for asset pricing models because they will not result in an asset return of less than -100%.

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Lognormal distribution returns are used for asset pricing models because this will not result in asset returns of less than 100% because the lowest the asset price can decrease to is zero which is the lowest value on the lognormal distribution. The normal distribution allows for asset prices less than zero which could result in a return of less than -100% which is impossible.

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

A) abnormally distributed.

B) defined as ex.

C) normally distributed.

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For any random variable that is normally distributed its natural logarithm (ln) will be lognormally distributed. The opposite is also true: for any random variable that is lognormally distributed its natural logarithm (ln) will be normally distributed.

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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.

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For any random variable that is lognormally distributed its natural logarithm (ln) will be normally distributed.

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Given Y is lognormally distributed, then ln Y is:

A) a lognormal distribution.

B) normally distributed.

C) the antilog of Y.

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If Y is lognormally distributed, then ln Y is normally distributed.