CFA Level 1 (2009) - 1

Scudy Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

One of the imponant applications of a binomial stock price model is in pricing options. We can make a binomial trec for asset prices more realistic by shortening the length of the periods and increasing the number of periods and possible outcomes.

LOS 9.e: Describe the continuous uniform distribution, and calculate and interpret probabilities, given a continuous uniform probability distribution.

The continuous uniform distribution is defined over a range that spans between some lower limit, d, and some upper limit, b, which serve as the parameters of the distribution. Outcomes can only occur between a and b, and since we are dealing with a continuous distribution, even if a < x < b, P(X = x) = O. FormaJJy, the properties of a continuous uniform distribution may be described as follows:

Don't miss how simple this is just because the notation is so mathematical. For a continuous uniform distribution, the probability of outcomes in a range that is one-half the whole range is 50%. The probability of outcomes in a range that is one-quarter as large as the whole possible range is 25%.

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

Example: Continuous uriiformdistribution

Xis uniformly distributed between 2 and 12. Calculate the probability that X will be

The figure below il1ust~atesiliiscontinuous uniform distribution. Note that the area bounded by 4 arldBis40% 'of the total probability between 2 and 12 (which is 100%).

Continuous Uniform Distribution

Probabilitv

Figure 4: CDF for a Continuous Uniform Variable

1.0

0,50

0.20

2 4 6 8 10 12

Since outcomes are equal over equal-size possible intervals, the cumulative distribution function (CDF) is linear over the variable's range. The CDF for the distribution in the above example, Prob (X < x), is shown in Figure 4.

LOS 9.f: Explain the key properties of the normal distribution, distinguish between a univariate and a multivariate distribution, and explain the role of correlation in the multivariate normal distribution.

The normal distribution is important for many reasons. Besides the high probability that it will be covered on the exam, many of the random variables that are relevant to finance and other professional disciplines follow a normal distribution. In the area of investment and portfolio management, the normal distribution plays a central role in portfolio theory.

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

The normal distribution has the following key properties:

•It is completely described by its mean, ~, and variance, 0 2, stated as X ~ N(~, 0 2). In

words, this says that "X is normally distributed with mean ~ and variance 0 2 ."

•Skewness = 0, meaning that the normal distribution is symmetric about its me:ln, so

that P(X :s; ~) = P(~ :s; X) = 0.5, and mean = median = mode.

•Kurrosis = 3; this is a measure of how flat the distribution is. Recall that excess

kurtosis is measured relative to 3, the kurtosis of the normal distribution.

A linear combination of normally distributed random variables is also normally distributed.

•The probabilities of outcomes further above and below the mean get smaller and smaller but do not go ro zero (the tails get very thin but extend infinitely).

Many of these properties are evident from examining the graph of a normal distribution's probability density function as illustrated in Figure 5.

Figure 5: Normal Distribution Probability Density Function

The normal curve is symmctrical.

The rwo halves ;IfC identical.

Theorctically, the

CUfI'C extends to - 00.

The mean, median, and mode are equal.

Univariate and Multivariate Distributions

Up to this point, our discussion has been strictly focused on univariate distributions, (i.e., the distribution of a single random variable). In practice, however, the relationships between two or more random variables are often relevant. For instance, investors and investment managers are frequently interested in the interrelationship among the

rerurns of one or more assets. In fact, as you will see in your study of asset pricing models and modern portfolio theory, the rerum on a given srock and the return on the S&P 500 or some other market index will have special significance. Regardless of the specific variables, the simultaneous analysis of two or more random variables requires an understanding of multivariate distributions.

A multivariate distribution specifies the probabilities associated with a group of random variables and is meaningful only when the behavior of each random variable in the group is in some way dependent upon the behavior of the others. Both discrete and continuous random variables can have multivariate distributions. Multivariate

distributions between two discrete random variables are described using joint probability tables. For continuous random variables, a multivariate normal distribution may be

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

used to describe them if all of the individual variables follow a normal distribution. As previously mentioned, one of the characteristics of a normal distribution is that a linear combination of normally distributed random variables is normally distributed as well. For example, if the return of each stock in a portfolio is normally distributed, the rerurn on the portfolio will also be normally distributed.

The Role of Correlation in the Multivariate Normal Distribution

Similar to a univariate normal distribution, a multivariate normal distribution can be described by the mean and variance of the individual random variables. Additionally, it is necessary to specify the correlation between the individual pairs of variables when describing a multivariate distribution. Correlation is the feature that distinguishes a

multivariate distribution from a univariate normal distribution. Correlation indicates tht' strength ofthe linear relationship between a pair of random variables.

Using assct returns as our random variables, the mulrivariar(' normal disrribution for the rerurns on /I assers can be complcrely defined by rhc following rhree scrs of p,lramctcrs:

•I1l11eanS ofrhe 1/ series of rerurns (PI'P2'... , ~lJ)'

• n variances of rhe II series of returns (a}' (J~, ... , (J~).

•0.5n(n - I) pair-wisc correlarions.

For example, if rhere arc rwo assers, n = 2, rhen the mulrivariare rcrurns disrribution can be described wirh rwo means, rwo varianccs, and one corrdarion [0.5(2)(2 - 1) =

1]. If therc are four assers. n = 4. rhe mulrivariare distriburion can be described wirh four means, four variances, and six correlations [0.5(4)(4 - 1) = 61. When building a ponfolio of assets, all othcr things being equal, ir is desirable to combine assers having low returns correlarion because rhis will result in a ponfolio wirh a lowcr variance dun one composed of assers with higher correlations.

LOS 9.g: Construct and interpret a confidence interval for a normally distributed random variable, and determine the probability that a normally distributed random variable lies inside a given confidence interval.

A confidence interval is a range of values around the expected outcomc within which we expect the actual outcome to be some specified percentage of the time. A 95% confidence interval is a range thar we expect the random variable to be in 95% of the time. For a normal distribution, this interval is based on the expecred value (sometimes called a point estimate) of the random variable and on irs variability, which we measure with standard deviation.

Confidence intervals for a normal distribution are illustrated in Figure 6. For any normally distributed random variable, 68% of the outcomes are wirhin one standard deviation of the expected value (mean) and approximately 95% of the outcomes are within rwo standard deviations of the expected value.

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

Figure 6: Confidence Intervals for a Normal Distribution

Probability

In pracricc we will not know the actual values for the mcan and standard deviation of the distribution, but will havc cstimatcd them as Xand s. The three confidence inrervals of most inrerest are given by:

•The 99% confidence interlJal for X is X- 2.585 to X+ 2.58s.

Example: Confidence intervals

The average return of a mutual fund is 10.5%,per year and the standard deviation of annual returns is 18%. If returns are, approXimately normal, what is the 95% confidence interval for the mutualfundretu,rn'next year?

Answer:

HerelJ. and a are 10.5% and 18%, respectively. Thus, the 950/0confidenceiriteFvaI£or' the return, R, is:

10.5 ± 1.96(18) =-24.78% to 45.78%

Symbolically, this result can be expressed.aS:

P(-24.78 < R < 45.78) = 0.95 or 95%

The interpretation is that the annual returnois expected to be within this interval 95% of the time or 95 our of 100 years.

LOS 9.h: Define the standard normal distribution, explain how to standardize a random variable, and calculate and interpret probabilities using the standard normal distribution.

The standard normal distribution is a normal distribution that has been standardized so that it has a mean ofzero and a standard deviation of 1 [i.e., N~(O, 1)]. To standardize

Study Session 3

Cross-Reference to CPA Institute Assigned Reading #9 - Common Probability Distributions

an observation from a given normal distribution, the z-value of the observation must be calculated. The z-value represents the number of standard deviations a given observation is from the population mean. Standardization is the process of converting an observed value for a random variable to its z-value. The following formula is used to

random variable:

~Professor's Note: The term z-value will be used for a standardized observation i11

~this document. The terms z-score and z-statistic are also commonly used.

Example: Standardizing a random variable (calculating z-values)

Assume that the annual earnings per share (EPS) for a population of firms are normally distributed with a mean of $6.00 and a standard deviation of $2.00.

What are the z-values for EPS of $2.00 and $8.00?

Answer:

Here, z = +1 indicates that an EPS of $8 is one standard deviation above the mean, and z = -2 means that an EPSof $2 is two standard deviations below the mean.

Calculating Probabilities Using z-Values

Now we will show how to use standardized values (z-values) and a table of probabilities for Z to determine probabilities. A porrion of a table of the cumulative distribution function for a standard normal distribution is shown in Figure 7. We will refer to this table as the z-table, as it contains values generated using the cumulative density function for a standard normal distribution, denoted by F(Z). Thus, the values in the z-table are the probabilities of observing a z-value that is less than a given value, z (i.e., P(Z < z)). The numbers in the first column are z-values that have only one decimal place. The columns to the right supply probabilities for z-values with two decimal places.

Note that the z-table in Figure 7 only provides probabilities for positive z-values. This is not a problem because we know from the symmetry of the standard normal distribution that F(-Z) = 1 - F(Z). The tables in the back of many texts actually provide probabilities for negative z-values, but we will work with only the positive portion of the table because this may be all you get on the exam. In Figure 7 we can find the probability that a standard normal random variable will be less than 1.66, for example. The table value is 95.15%. The probability that the random variable will be less than -1.66 is simply 1- 0.9515 = 0.0485 =4.85%, which is also the probability that the variable will be greater than +1.66.

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

Professor sNote: When you use the standard normal probabilities, you have formulated the problem in terms ofstandard deviations from the mean. Consider a security with returns that are approximately normal, an expected return of 10%, and standard deviation ofreturns of 12%. The probability

~ofreturns greater than 30% is calculated based on the number ofstandard

~deviations that 30% is above the expected return of 10%.30% is 20% above

the expected return of 10%, which is 20/12 = 1.67 standard deviations above the mean. we look up the probability ofreturns less than 1.67 standard

deviations above the mean (0.9525 or 95.25% from Figure 7), and calculate

the probability ofreturns more than 1.67 standard deviations above the mean as 1 - 0.9525 = 4.75%.

Figure 7: Cumulative Probabilities for a Standard Normal Distribution

CdfValues for the Standard Normal Distribution: The z-table

0.5.6915 Please note that several of the rows have been deleted to save space.'

* A complete cumulative standard normal table is included in Appendix A.

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

::Here'~e'want,to'~Deow:P(EPS'~'.i$~}9);i:wh,j.d~:isthe area under the curve to the fight

Swdy Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

Answer:

As shown graphically in the figure below, we want to know P(EPS <$4.10). This requires a 2-step approach like the one taken in the preceding example.

First, the corresponding z-value must be determined as follows:

z = ($4.10-$6) = -0.95, so $4. lOis 0.95 standard deviations below the mean

Now. from the z-table for negative values in the back of this book, we find that

F(-0.95) = 0.1711. or 17.11%.

Finding a Left-Tail Probability

,

\ ,

\~

The z-table gave us the probability that the outcome will be more than 0.95 standard deviations below the mean.

LOS lJ .i: Denne shortfall risk, calculate the safety-first ratio, and select an optimal portfolio using Roy's safety.·first criterion.

Shortfall risk is the probability that a portfolio value or rerum will fall below a particular (target) value or return over a given time period.

Roy's safety-first criterion states that the optimal portfolio minimizes the probability that the return of the portfolio falls below some minimum acceptable level. This minimum acceptable level is called the "threshold" level. Symbolically, Roy's safety-first criterion can be stated as:

minimize P(Rp < RL)

where:

Rp = portfolio rerum

RL = threshold level rerum

Study Session 3

Cross-Reference to CFA Institute Assigned Reading #9 - Common Probability Distributions

If portfolio returns are normally distributed, then Roy's safety-first criterion can be stated as:

[E(Rp)-RL ]

maximize the SFRatio where SFRatio =

O"p

Professor 5 Note: Notice the similarity to the Sharpe ratio:

~Sharpe = [E(Rp)- Rf ] . The only difference is that the SFRatio utilizes the

~O"p

excess return over the threshold return, RL , where the Sharpe ratio uses the excess return over the risk-free rate, Rr. When the threshold level is the risk-free rate ofreturn, the SFRatio is also the Sharpe ratio.

The reasoning hehind the safcry-hm criterion is illustrated ill Figure R. Assume an investor is choosing hetween two portfolios: Portfolio A with expected return of 12% and standard deviation of returns of 18%, and Portfolio B with expected return of 10% and standard deviation of returns of 12%. The investor has stated that he wants co minimize the probability of losing money (negative returns). Assuming that rerurns are normally distrihured. rhe portfolio with the larger SFR usin; 0% as the threshold rerurn (RL) will be the one with the lower probability of negative returns.

Figure 8: The Safety-First Criterion and Shortfall Risk

A. Normally Distributed Rerurns

Probability of rerurns < 0% - i.e. shorr fall risk

B. Standard Normal

2S1~ 201~

Panel B of Figure 8 relates the SFRatio co the standard normal distribution. Note that the SFR is the number of standard deviations below the mean. Thus, the portfolio with the larger SFR has the lower probability of returns below the threshold return, which