# Review of Random Variables

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1 Chapter 1 Review of Random Variables Updated: January 16, 2015 This chapter reviews basic probability concepts that are necessary for the modeling and statistical analysis of financial data. 1.1 Random Variables We start with the basic definition of a random variable: Definition 1 A Random variable is a variable that can take on a given set of values, called the sample space and denoted, where the likelihood of the values in is determined by s probability distribution function (pdf). Example 2 Future price of Microsoft stock Consider the price of Microsoft stock next month. Since the price of Microsoft stock next month is not known with certainty today, we can consider it a randomvariable. Thepricenextmonthmustbepositiveandrealistically it can t get too large. Therefore the sample space is the set of positive real numbers bounded above by some large number: = { : [0 ] 0}. Itisanopenquestionastowhatisthebestcharacterizationofthe probability distribution of stock prices. The log-normal distribution is one possibility. 1 1 If is a positive random variable such that ln is normally distributed then has a log-normal distribution. 1

2 2 CHAPTER 1 REVIEW OF RANDOM VARIABLES Example 3 Return on Microsoft stock Consider a one-month investment in Microsoft stock. That is, we buy one share of Microsoft stock at the end of month 1 (e.g., end of February) andplantosellitattheendofmonth (e.g., end of March). The return over month on this investment, =( 1 ) 1 is a random variable becausewedonotknowwhatthepricewillbeattheendofthemonth. In contrast to prices, returns can be positive or negative and are bounded from below by -100%. We can express the sample space as = { : [ 1 ] 0} The normal distribution is often a good approximation to the distribution of simple monthly returns, and is a better approximation to the distribution of continuously compounded monthly returns. Example 4 Up-down indicator variable As a final example, consider a variable definedtobeequaltooneifthe monthly price change on Microsoft stock, 1 is positive, and is equal to zero if the price change is zero or negative. Here, the sample space is the set = {0 1} If it is equally likely that the monthly price change is positive or negative (including zero) then the probability that =1or =0is 0 5 This is an example of a bernoulli random variable. The next sub-sections define discrete and continuous random variables Discrete Random Variables Consider a random variable generically denoted and its set of possible values or sample space denoted Definition 5 A discrete random variable is one that can take on a finite number of different values = { 1 2 } or, at most, a countably infinite number of different values = { 1 2 } Definition 6 The pdf of a discrete random variable, denoted ( ) is a function such that ( ) =Pr( = ) The pdf must satisfy (i) ( ) 0 for all ; (ii) ( ) =0for all ; and (iii) P ( ) =1 Example 7 Annual return on Microsoft stock

3 1.1 RANDOM VARIABLES 3 State of Economy = Sample Space ( ) =Pr( = ) Depression Recession Normal Mild Boom Major Boom Table 1.1: Probability distribution for the annual return on Microsoft Let denote the annual return on Microsoft stock over the next year. We might hypothesize that the annual return will be influenced by the general state of the economy. Consider five possible states of the economy: depression, recession, normal, mild boom and major boom. A stock analyst might forecast different values of the return for each possible state. Hence, is a discrete random variable that can take on five different values. Table 1.1 describes such a probability distribution of the return and a graphical representation of the probability distribution is presented in Figure 1.1. The Bernoulli Distribution Let =1if the price next month of Microsoft stock goes up and =0if the price goes down (assuming it cannot stay the same). Then is clearly a discrete random variable with sample space = {0 1} If the probability ofthestockpricegoingupordownisthesamethen (0) = (1) = 1 2 and (0) + (1) = 1 The probability distribution described above can be given an exact mathematical representation known as the Bernoulli distribution. Consider two mutually exclusive events generically called success and failure. For example, a success could be a stock price going up or a coin landing heads and a failure could be a stock price going down or a coin landing tails. The process creating the success or failure is called a Bernoulli trial. In general, let =1 if success occurs and let =0if failure occurs. Let Pr( =1)= where 0 1, denote the probability of success. Then Pr( =0)=1 is the

4 4 CHAPTER 1 REVIEW OF RANDOM VARIABLES probability return Figure 1.1: Discrete distribution for Microsoft stock. probability of failure. A mathematical model describing this distribution is ( ) =Pr( = ) = (1 ) 1 =0 1 (1.1) When =0 (0) = 0 (1 ) 1 0 =1 and when =1 (1) = 1 (1 ) 1 1 = The Binomial Distribution Consider a sequence of independent Bernoulli trials with success probability generating a sequence of 0 1 variables indicating failures and successes. A binomial random variable counts the number of successes in Bernoulli trials, and is denoted ( ). The sample space is = {0 1 } and µ Pr( = ) = (1 )

5 1.1 RANDOM VARIABLES 5 The term is the binomial coefficient, and counts the number of ways objects can be chosen from distinct objects. It is defined by µ =! ( )!! where! is the factorial of, or ( 1) 2 1 Example 8 Binomial tree model for stock prices To be completed Continuous Random Variables Definition 9 A continuous random variable X is one that can take on any real value. That is, = { : R} Definition 10 The probability density function (pdf) of a continuous random variable is a nonnegative function defined on the real line, such that for any interval Z Pr( ) = ( ) That is, Pr( ) is the area under the probability curve over the interval The pdf ( ) must satisfy (i) ( ) 0; and (ii) R ( ) =1 A typical bell-shaped pdf is displayed in Figure 1.2 and the area under the curve between 2 and 1 represents Pr( 2 1). For a continuous random variable, ( ) 6= Pr( = ) but rather gives the height of the probability curve at In fact, Pr( = ) =0for all values of That is, probabilities are not defined over single points. They are only defined over intervals. As a result, for a continuous random variable we have Pr( ) =Pr( ) =Pr( )=Pr( )

6 6 CHAPTER 1 REVIEW OF RANDOM VARIABLES pdf x Figure 1.2: Pr( 2 1) is represented by the area under the probability curve. The Uniform Distribution on an Interval Let denote the annual return on Microsoft stock and let and be two real numbers such that. Suppose that the annual return on Microsoft stock can take on any value between and. That is, the sample space is restricted to the interval = { R : } Further suppose that the probability that will belong to any subinterval of is proportional to the length of the interval. In this case, we say that is uniformly distributed on the interval [ ] The pdf of has a very simple mathematical form: ( ) = 1 0 for otherwise, and is presented graphically in Figure 1.3. Notice that the area under the curve over the interval [ ] (area of rectangle) integrates to one: Z 1 = 1 Z = 1 [ ] = 1 [ ] =1

7 1.1 RANDOM VARIABLES 7 pdf u Figure 1.3: Uniform distribution over the interval [0,1]. That is, (0 1). Example 11 Uniform distribution on [ 1 1] Let = 1 and =1 so that =2. Consider computing the probability that the return will be between -50% and 50%.We solve Pr( 50% 50%) = Z = 1 2 [ ] = 1 2 [0 5 ( 0 5)] = 1 2 Next, consider computing the probability that the return will fall in the interval [0 ] where is some small number less than =1: Pr(0 ) = 1 2 Z 0 = 1 2 [ ] 0 = 1 2 As 0 Pr(0 ) Pr( =0) Usingtheaboveresultweseethat 1 lim Pr(0 ) =Pr( =0)=lim =0 Hence, probabilities are defined on intervals but not at distinct points.

8 8 CHAPTER 1 REVIEW OF RANDOM VARIABLES The Standard Normal Distribution The normal or Gaussian distribution is perhaps the most famous and most useful continuous distribution in all of statistics. The shape of the normal distribution is the familiar bell curve. As we shall see, it can be used to describe the probabilistic behavior of stock returns although other distributions may be more appropriate. If a random variable follows a standard normal distribution then we often write (0 1) as short-hand notation. This distribution is centered at zero and has inflection points at ±1 2 The pdf of a normal random variable is given by ( ) = (1.2) 2 It can be shown via the change of variables formula in calculus that the area under the standard normal curve is one: Z =1 2 The standard normal distribution is illustrated in Figure 1.4. Notice that the distribution is symmetric about zero; i.e., the distribution has exactly the same form to the left and right of zero. The normal distribution has the annoying feature that the area under the normal curve cannot be evaluated analytically. That is Z 1 Pr( ) = doesnothaveaclosedformsolution.theaboveintegralmustbecomputed by numerical approximation. Areas under the normal curve, in one form or another, are given in tables in almost every introductory statistics book and standard statistical software can be used to find these areas. Some useful approximate results are: Pr( 1 1) 0 67 Pr( 2 2) 0 95 Pr( 3 3) An inflection point is a point where a curve goes from concave up to concave down, or vice versa.

9 1.1 RANDOM VARIABLES 9 pdf x Figure 1.4: Standard normal density The Cumulative Distribution Function Definition 12 The cumulative distribution function (cdf) of a random variable (discrete or continuous), denoted is the probability that : The cdf has the following properties: ( ) =Pr( ) (i) If 1 2 then ( 1 ) ( 2 ) (ii) ( ) =0and ( ) =1 (iii) Pr( )=1 ( ) (iv) Pr( 1 2 )= ( 2 ) ( 1 )

10 10 CHAPTER 1 REVIEW OF RANDOM VARIABLES (v) ( ) 0 = ( ) = ( ) if is a continuous random variable and ( ) is continuous and differentiable. Example 13 ( ) for a discrete random variable The cdf for the discrete distribution of Microsoft from Table 1.1 is given by ( ) = and is illustrated in Figure 1.5. Example 14 ( ) for a uniform random variable The cdf for the uniform distribution over [ ] can be determined analytically: ( ) =Pr( )= = 1 Z Z ( ) = 1 [ ] = We can determine the pdf of directly from the cdf via ( ) = ( ) 0 = ( ) = 1 Example 15 ( ) for a standard normal random variable The cdf of standard normal random variable is used so often in statistics that it is given its own special symbol: Z 1 Φ( ) = ( ) = (1.3) 2 The cdf Φ( ), however, does not have an analytic representation like the cdf of the uniform distribution and so the integral in (1.3) must be approximated using numerical techniques. A graphical representation of Φ( ) is given in Figure 1.6.

11 1.1 RANDOM VARIABLES 11 cdf x.vals Figure 1.5: CDF of Discrete Distribution for Microsoft Stock Return Quantiles of the Distribution of a Random Variable Consider a random variable with continuous cdf ( ) For 0 1 the 100 % quantile of the distribution for is the value that satisfies ( )=Pr( )= For example, the 5% quantile of 0 05 satisfies ( 0 05 )=Pr( 0 05 )=0 05 The median of the distribution is 50% quantile. That is, the median, 0 5 satisfies ( 0 5 )=Pr( 0 5 )=0 5 If is invertible 3 then may be determined analytically as = 1 ( ) (1.4) 3 The inverse of ( ) will exist if is strictly increasing and is continuous.

12 12 CHAPTER 1 REVIEW OF RANDOM VARIABLES CDF x Figure 1.6: Standard normal cdf Φ( ) where 1 denotes the inverse function of Hence, the 5% quantile and the median may be determined from In inverse cdf = 1 ( 05) 0 5 = 1 ( 5) is sometimes called the quantile function. Example 16 Quantiles from a uniform distribution Let [ ] where Recall, the cdf of is given by ( ) = which is continuous and strictly increasing. ( ) =, solvingfor gives the inverse cdf: Given [0 1] such that = 1 ( ) = ( )+ (1.5)

13 1.1 RANDOM VARIABLES 13 Using (1.5), the 5% quantile and median, for example, are given by = 1 ( 05) = 05( )+ = = 1 ( 5) = 5( )+ = 5( + ) If =0and =1 then 0 05 =0 05 and 0 5 =0 5 Example 17 Quantiles from a standard normal distribution Let (0 1) The quantiles of the standard normal distribution are determined by solving = Φ 1 ( ) (1.6) where Φ 1 denotes the inverse of the cdf Φ This inverse function must be approximated numerically and is available in most spreadsheets and statistical software. Using the numerical approximation to the inverse function, the 1%, 2.5%, 5%, 10% quantiles and median are given by = Φ 1 ( 01) = = Φ 1 ( 025) = 1 96 = Φ 1 ( 05) = = Φ 1 ( 10) = 1 28 = Φ 1 ( 5) = 0 Often, the standard normal quantile is denoted R Functions for Discrete and Continuous Distributions R has built-in functions for a number of discrete and continuous distributions. These are summarized in Table 1.2. For each distribution, there are four functions starting with d, p, q and r that compute density (pdf) values, cumulative probabilities (cdf), quantiles (inverse cdf) and random draws, respectively. Consider, for example, the functions associated with the normal distribution. The functions dnorm(), pnorm() and qnorm() evaluate the standard normal density (1.2), the cdf (1.3), and the inverse cdf or quantile function (1.6), respectively, with the default values mean=0 and sd = 1. The function rnorm() returns a specified number of simulated values from the normal distribution.

14 14 CHAPTER 1 REVIEW OF RANDOM VARIABLES Distribution Function (root) Parameters Defaults beta beta shape1, shape2 _, _ binomial binom size, prob _, _ Cauchy cauchy location, scale 0, 1 chi-squared chisq df, ncp _, 1 F f df1, df2 _, _ gamma gamma shape, rate, scale _, 1, 1/rate geometric geom prob _ hyper-geometric hyper m, n, k _, _, _ log-normal lnorm meanlog, sdlog 0, 1 logistic logis location, scale 0, 1 negative binomial nbinom size, prob, mu _, _, _ normal norm mean, sd 0, 1 Poisson pois Lambda 1 Student s t t df, ncp _, 1 uniform unif min, max 0, 1 Weibull weibull shape, scale _, 1 Wilcoxon wilcoxon m, n _, _ Table 1.2: Probability distributions in base R.

15 1.1 RANDOM VARIABLES 15 Finding Areas Under the Normal Curve Most spreadsheet and statistical software packages have functions for finding areas under the normal curve. Let denote a standard normal random variable. Some tables and functions give Pr(0 ) for various values of 0 some give Pr( ) and some give Pr( ) Given that the total area under the normal curve is one and the distribution is symmetric about zero the following results hold: Pr( ) =1 Pr( ) and Pr( ) =1 Pr( ) Pr( ) =Pr( ) Pr( 0) = Pr( 0) = 0 5 The following examples show how to compute various probabilities. Example 18 Finding areas under the normal curve using R First, consider finding Pr( 2). By the symmetry of the normal distribution, Pr( 2) = Pr( 2) = Φ( 2) In R use > pnorm(-2) [1] Next, consider finding Pr( 1 2) Using the cdf, we compute Pr( 1 2) = Pr( 2) Pr( 1) = Φ(2) Φ( 1) In R use > pnorm(2) - pnorm(-1) [1] Finally, using R the exact values for Pr( 1 1) Pr( 2 2) and Pr( 3 3) are > pnorm(1) - pnorm(-1) [1] > pnorm(2) - pnorm(-2) [1] > pnorm(3) - pnorm(-3) [1]

16 16 CHAPTER 1 REVIEW OF RANDOM VARIABLES Plotting Distributions When working with a probability distribution, it is a good idea to make plots of the pdf or cdf to reveal important characteristics. The following examples illustrate plotting distributions using R. Example 19 Plotting the standard normal curve The graphs of the standard normal pdf and cdf in Figures 1.4 and 1.6 were created using the following R code: # plot pdf > x.vals = seq(-4, 4, length=150) > plot(x.vals, dnorm(x.vals), type="l", lwd=2, col="blue", + xlab="x", ylab="pdf") # plot cdf > plot(x.vals, pnorm(x.vals), type="l", lwd=2, col="blue", + xlab="x", ylab="cdf") Example 20 Shading a region under the standard normal curve Figure 1.2 showing Pr( 2 1) as a red shaded area is created with the following code >lb=-2 >ub=1 > x.vals = seq(-4, 4, length=150) > d.vals = dnorm(x.vals) # plot normal density > plot(x.vals, d.vals, type="l", xlab="x", ylab="pdf") >i=x.vals>=lb&x.vals<=ub # add shaded region between -2 and 1 > polygon(c(lb, x.vals[i], ub), c(0, d.vals[i], 0), col="red")

17 1.1 RANDOM VARIABLES Shape Characteristics of Probability Distributions Very often we would like to know certain shape characteristics of a probability distribution. We might want to know where the distribution is centered, and how spread out the distribution is about the central value. We might want to know if the distribution is symmetric about the center or if the distribution has a long left or right tail. For stock returns we might want to know about the likelihood of observing extreme values for returns representing market crashes. This means that we would like to know about the amount of probability in the extreme tails of the distribution. In this section we discuss four important shape characteristics of a probability distribution: 1. expected value (mean): measures the center of mass of a distribution 2. variance and standard deviation: measures the spread about the mean 3. skewness: measures symmetry about the mean 4. kurtosis: measures tail thickness Expected Value The expected value of a random variable denoted [ ] or measures the center of mass of the pdf. For a discrete random variable with sample space, the expected value is defined as = [ ] = X Pr( = ) (1.7) Eq. (1.7) shows that [ ] is a probability weighted average of the possible values of Example 21 Expected value of discrete random variable Using the discrete distribution for the return on Microsoft stock in Table 1.1, the expected return is computed as: [ ] =( 0 3) (0 05) + (0 0) (0 20) + (0 1) (0 5) + (0 2) (0 2) + (0 5) (0 05) =0 10

18 18 CHAPTER 1 REVIEW OF RANDOM VARIABLES Example 22 Expected value of Bernoulli and binomial random variables Let be a Bernoulli random variable with success probability Then [ ] =0 (1 )+1 = That is, the expected value of a Bernoulli random variable is its probability of success. Now, let ( ) It can be shown that [ ]= X µ (1 ) = (1.8) =0 For a continuous random variable with pdf ( ) theexpectedvalueis defined as Z = [ ] = ( ) (1.9) Example 23 Expected value of a uniform random variable Suppose has a uniform distribution over the interval [ ] Then Z [ ] = 1 = 1 1 = 2 2 2( ) ( )( + ) = 2( ) If = 1 and =1 then [ ] =0 = Example 24 Expected value of a standard normal random variable Let (0 1). Then it can be shown that [ ] = Z =0 Hence, the standard normal distribution is centered at zero.

19 1.1 RANDOM VARIABLES 19 Expectation of a Function of a Random Variable The other shape characteristics of the distribution of a random variable are based on expectations of certain functions of. Let ( ) denote some function of the random variable. If is a discrete random variable with sample space then [ ( )] = X ( ) Pr( = ) (1.10) and if is a continuous random variable with pdf then [ ( )] = Z Variance and Standard Deviation ( ) ( ) (1.11) Thevarianceofarandomvariable, denoted var( ) or 2 measures the spread of the distribution about the mean using the function ( ) =( ) 2 If most values of are close to then on average ( ) 2 will be small. In contrast, if many values of are far below and/or far above then on average ( ) 2 will be large Squaring the deviations about guarantees a positive value. The variance of is defined as 2 =var( ) = [( ) 2 ] (1.12) Because 2 represents an average squared deviation, it is not in the same units as The standard deviation of denoted sd( ) or is the square root of the variance and is in the same units as. For bell-shaped distributions, measures the typical size of a deviation from the mean value. The computation of (1.12) can often be simplified by using the result var( ) = [( ) 2 ]= [ 2 ] 2 (1.13) Example 25 Variance and standard deviation for a discrete random variable Using the discrete distribution for the return on Microsoft stock in Table 1.1 and the result that =0 1, wehave var( ) =( ) 2 (0 05) + ( ) 2 (0 20) + ( ) 2 (0 5) +( ) 2 (0 2) + ( ) 2 (0 05) =0 020

20 20 CHAPTER 1 REVIEW OF RANDOM VARIABLES Alternatively, we can compute var( ) using (1.13) [ 2 ] 2 =( 0 3) 2 (0 05) + (0 0) 2 (0 20) + (0 1) 2 (0 5) +(0 2) 2 (0 2) + (0 5) 2 (0 05) (0 1) 2 =0 020 The standard deviation is sd( ) = = = Given that the distribution is fairly bell-shaped we can say that typical values deviate from the mean value of 10% by about 14.1%. Example 26 Variance and standard deviation of a Bernoulli and binomial random variables Let be a Bernoulli random variable with success probability Given that = it follows that var( ) =(0 ) 2 (1 )+(1 ) 2 = 2 (1 )+(1 ) 2 = (1 )[ +(1 )] = (1 ) sd( ) = p (1 ) Now, let ( ) It can be shown that var( )= (1 ) and so sd( )= p (1 ) Example 27 Variance and standard deviation of a uniform random variable Let [ ] Using (1.13) and = +, after some algebra, it can be 2 shown that var( ) = [ 2 ] 2 = 1 Z µ = ( )2 and sd( ) =( ) 12 Example 28 Variance and standard deviation of a standard normal random variable Let (0 1) Here, =0and it can be shown that 2 = It follows that sd( ) =1 Z =1

21 1.1 RANDOM VARIABLES 21 The General Normal Distribution Recall, if has a standard normal distribution then [ ] =0 var( ) =1 A general normal random variable has [ ] = and var( ) = 2 and is denoted ( 2 ) Its pdf is given by ( ) = 1 p 2 2 exp ½ 1 ¾ ( 2 2 ) 2 (1.14) Showing that [ ] = and var( ) = 2 is a bit of work and is good calculus practice. As with the standard normal distribution, areas under the general normal curve cannot be computed analytically. Using numerical approximations, it can be shown that Pr( Pr( 2 Pr( 3 + ) ) ) 0 99 Hence, for a general normal random variable about 95% of the time we expect to see values within ± 2 standard deviations from its mean. Observations more than three standard deviations from the mean are very unlikely. Example 29 Normal distribution for monthly returns Let denote the monthly return on an investment in Microsoft stock, and assume that it is normally distributed with mean =0 01 and standard deviation =0 10 That is, (0 01 (0 10) 2 ) Notice that 2 =0 01 and is not in units of return per month. Figure 1.7 illustrates the distribution. Notice that essentially all of the probability lies between 0 4 and 0 4 Using the R function pnorm(), we can easily compute the probabilities Pr( 0 5) Pr( 0) Pr( 0 5) and Pr( 1): > pnorm(-0.5, mean=0.01, sd=0.1) [1] 1.698e-07 > pnorm(0, mean=0.01, sd=0.1) [1] >1-pnorm(0.5,mean=0.01,sd=0.1) [1] 4.792e-07 >1-pnorm(1,mean=0.01,sd=0.1) [1] 0

22 22 CHAPTER 1 REVIEW OF RANDOM VARIABLES pdf x Figure 1.7: Normal distribution for the monthly returns on Microsoft: (0 01 (0 10) 2 ) Using the R function qnorm(), wecanfind the quantiles and 0 99 : > a.vals = c(0.01, 0.05, 0.95, 0.99) > qnorm(a.vals, mean=0.01, sd=0.10) [1] Hence, over the next month, there are 1% and 5% chances of losing more than 22.2% and 15.5%, respectively. In addition, there are 5% and 1% chances of gaining more than 17.5% and 24.3%, respectively. Example 30 Risk-return tradeoff Consider the following investment problem. We can invest in two nondividend paying stocks, Amazon and Boeing, over the next month. Let

23 1.1 RANDOM VARIABLES 23 pdf Amazon Boeing x Figure 1.8: Risk-return tradeoff between one-month investments in Amazon and Boeing stock. denote the monthly return on Amazon and denote the monthly return on Boeing. Assume that (0 02 (0 10) 2 ) and (0 01 (0 05) 2 ) Figure 1.8 shows the pdfs for the two returns. Notice that =0 02 = 0 01 but also that =0 10 =0 05. The return we expect on Amazon is bigger than the return we expect on Boeing but the variability Amazon s return is also greater than the variability of Boeing s return. The high return variability (volatility) of Amazon reflects the higher risk associated with investing in Amazon compared to investing in Boeing. If we invest in Boeing we get a lower expected return, but we also get less return variability or risk. This example illustrates the fundamental no free lunch principle of economics and finance: you can t get something for nothing. In general, to get a higher expected return you must be prepared to take on higher risk. Example 31 Why the normal distribution may not be appropriate for simple returns

24 24 CHAPTER 1 REVIEW OF RANDOM VARIABLES Let denote the simple annual return on an asset, and suppose that (0 05 (0 50) 2 ) Because asset prices must be non-negative, must always be larger than 1 However, the normal distribution is defined for and based on the assumed normal distribution Pr( 1) = That is, there is a 1.8% chance that is smaller than 1 This implies that there is a 1.8% chance that the asset price at the end of the year will be negative! Thisiswhythenormaldistribution may not appropriate for simple returns. Example 32 The normal distribution is more appropriate for continuously compounded returns Let =ln(1+ ) denote the continuously compounded annual return on an asset, and suppose that (0 05 (0 50) 2 ) Unlike the simple return, the continuously compounded return can take on values less than 1 In fact, is defined for For example, suppose = 2 This implies asimplereturnof = 2 1= Then Pr( 2) = Pr( 0 865) = Although the normal distribution allows for values of smaller than 1 the implied simple return will always be greater than 1 The Log-Normal distribution Let ( 2 ) which is defined for The log-normally distributed random variable is determined from the normally distributed random variable using the transformation = In this case, we say that is log-normally distributed and write ln ( 2 ) 0 (1.15) The pdf of the log-normal distribution for can be derived from the normal distribution for using the change-of-variables formula from calculus and is given by 1 ( ) = (ln ) (1.16) 2 4 If = then = 1= 1=0 1= 1

25 1.1 RANDOM VARIABLES 25 Due to the exponential transformation, is only defined for non-negative values. It can be shown that = [ ]= (1.17) 2 =var( )= ( 2 1) Example 33 Log-normal distribution for simple returns Let =ln( 1 ) denote the continuously compounded monthly return on an asset and assume that (0 05 (0 50) 2 ) That is, =0 05 and = 0 50 Let = 1 denote the simple monthly return. The relationship between and is given by =ln(1+ ) and 1+ = Since is normally distributed 1+ is log-normally distributed. Notice that the distribution of 1+ is only defined for positive values of 1+ This is appropriate since the smallest value that cantakeonis 1 Using (1.17), the mean and variance for 1+ are given by 1+ = 0 05+(0 5)2 2 = = 2(0 05)+(0 5)2 ( (0 5)2 1) = The pdfs for and 1+ are shown in Figure 1.9. Skewness The skewness of a random variable denoted skew( ) measures the symmetry of a distribution about its mean value using the function ( ) = ( ) 3 3 where 3 is just sd( ) raised to the third power: skew( ) = [( ) 3 ] (1.18) 3 When is far below its mean ( ) 3 is a big negative number, and when is far above its mean ( ) 3 is a big positive number. Hence, if there are more big values of below then skew( ) 0 Conversely, if there are more big values of above then skew( ) 0 If has a symmetric distribution then skew( ) =0since positive and negative values in (1.18) cancel out. If skew( ) 0 then the distribution of has a long right tail and if skew( ) 0 the distribution of has a long left tail. These cases are illustrated in Figure xxx. insert figure xxx here

26 26 CHAPTER 1 REVIEW OF RANDOM VARIABLES Normal Log-Normal pdf R Figure 1.9: Normal distribution for and log-normal distribution for 1+ = Example 34 Skewness for a discrete random variable Using the discrete distribution for the return on Microsoft stock in Table 1, the results that =0 1 and =0 141, wehave skew( ) =[( ) 3 (0 05) + ( ) 3 (0 20) + ( ) 3 (0 5) +( ) 3 (0 2) + ( ) 3 (0 05)] (0 141) 3 =0 0 Example 35 Skewness for a normal random variable Suppose has a general normal distribution with mean and variance 2.Thenitcanbeshownthat Z ( skew( ) = ) ( ) 2 =0 3

27 1.1 RANDOM VARIABLES 27 This result is expected since the normal distribution is symmetric about it s mean value Example 36 Skewness for a log-normal random variable Let = where ( 2 ) be a log-normally distributed random variable with parameters and 2 Then it can be shown that p skew( )= ³ Notice that skew( ) is always positive, indicating that the distribution of hasalongrighttail,andthatitisanincreasingfunctionof 2 This positive skewness is illustrated in Figure 1.9. Kurtosis The kurtosis of a random variable denoted kurt( ) measures the thickness in the tails of a distribution and is based on ( ) =( ) 4 4 : kurt( ) = [( ) 4 ] (1.19) 4 where 4 is just sd( ) raised to the fourth power. Since kurtosis is based on deviations from the mean raised to the fourth power, large deviations get lots of weight. Hence, distributions with large kurtosis values are ones where there is the possibility of extreme values. In contrast, if the kurtosis is small then most of the observations are tightly clustered around the mean and there is very little probability of observing extreme values. Figure xxx illustrates distributions with large and small kurtosis values. InsertFigureHere Example 37 Kurtosis for a discrete random variable Using the discrete distribution for the return on Microsoft stock in Table 1, theresultsthat =0 1 and =0 141, wehave kurt( ) =[( ) 4 (0 05) + ( ) 4 (0 20) + ( ) 4 (0 5) +( ) 4 (0 2) + ( ) 4 (0 05)] (0 141) 4 =6 5

28 28 CHAPTER 1 REVIEW OF RANDOM VARIABLES Example 38 Kurtosis for a normal random variable Suppose has a general normal distribution mean and variance 2. Then it can be shown that kurt( ) = Z ( ) p ( ) 2 =3 Hence a kurtosis of 3 is a benchmark value for tail thickness of bell-shaped distributions. If a distribution has a kurtosis greater than 3 then the distribution has thicker tails than the normal distribution and if a distribution has kurtosis less than 3 then the distribution has thinner tails than the normal. Sometimes the kurtosis of a random variable is described relative to the kurtosis of a normal random variable. This relative value of kurtosis is referred to as excess kurtosis and is defined as ekurt( ) = kurt( ) 3 (1.20) If the excess kurtosis of a random variable is equal to zero then the random variable has the same kurtosis as a normal random variable. If excess kurtosis is greater than zero, then kurtosis is larger than that for a normal; if excess kurtosis is less than zero, then kurtosis is less than that for a normal. The Student s-t Distribution Thekurtosisofarandomvariablegives information on the tail thickness of its distribution. The normal distribution, with kurtosis equal to three, gives a benchmark for the tail thickness of symmetric distributions. A distribution similar to the standard normal distribution but with fatter tails, and hence larger kurtosis, is the Student s t distribution. If has a Student s t distribution with degrees of freedom parameter, denoted then its pdf has the form ( ) = Γ +1 2 Γ 2 ( µ ) 0 (1.21)

29 1.1 RANDOM VARIABLES 29 pdf v=1 v=5 v=10 v= x Figure 1.10: Student s t density with = and 60 where Γ( ) = R 0 1 denotes the gamma function. It can be shown that [ ] =0 1 var( ) = 2 2 skew( ) =0 3 kurt( ) = The parameter controls the scale and tail thickness of distribution. If is close to four, then the kurtosis is large and the tails are thick. If 4 then kurt( ) = As the Student s t pdf approaches that of a standard normal random variable and kurt( ) =3. Figure 1.10 shows plots of the Student s t density for various values of as well as the standard normal density. Example 39 Computing tail probabilities and quantiles from the Student s

30 30 CHAPTER 1 REVIEW OF RANDOM VARIABLES t distribution The R functions pt() and qt() can be used to compute the cdf and quantiles of a Student s t random variable. For = and the 1% quantiles can be computed using >v=c(1,2,5,10,60,100,inf) > qt(0.01, df=v) [1] For = and thevaluesofpr( 3) are > pt(-3, df=v) [1] Linear Functions of a Random Variable Let be a random variable either discrete or continuous with [ ] = var( ) = 2 and let and be known constants. Define a new random variable via the linear function of Then the following results hold: = ( ) = + 2 = [ ]= [ ]+ = + =var( )= 2 var( ) = 2 2 =sd( )= sd( ) = The first result shows that expectation is a linear operation. That is, [ + ] = [ ]+ The second result shows that adding a constant to does not affect its variance, and that the effect of multiplying by the constant increases thevarianceof bythesquareof These results will be used often enough that it instructive to go through the derivations.

31 1.1 RANDOM VARIABLES 31 Consider the first result. Let be a discrete random variable. By the definition of [ ( )] with ( ) = + we have [ ]= X ( + ) Pr( = ) = X X Pr( = )+ Pr( = ) = [ ]+ 1 = + If is a continuous random variable then by the linearity of integration [ ]= Z = [ ]+ ( + ) ( ) = Z ( ) + Z Next consider the second result. Since = + we have var( )= [( ) 2 ] = [( + ( + )) 2 ] = [( ( )+( )) 2 ] = [ 2 ( ) 2 ] = 2 [( ) 2 ] (by the linearity of [ ]) = 2 var( ) ( ) Notice that the derivation of the second result works for discrete and continuous random variables. A normal random variable has the special property that a linear function of it is also a normal random variable. The following proposition establishes the result. Proposition 40 Let ( 2 ) and let and be constants. Let = + Then ( ) The above property is special to the normal distribution and may or may not hold for a random variable with a distribution that is not normal. Example 41 Standardizing a Random Variable

32 32 CHAPTER 1 REVIEW OF RANDOM VARIABLES Let be a random variable with [ ] = and var( ) = 2 Define a new random variable as = = 1 which is a linear function + where = 1 and = This transformation is called standardizing the random variable since [ ] = 1 [ ] = 1 =0 µ 2 1 var( ) = var( ) = 2 =1 2 Hence, standardization creates a new random variable with mean zero and variance 1. In addition, if is normally distributed then (0 1) Example 42 Computing probabilities using standardized random variables Let (2 4) andsupposewewanttofind Pr( 5) butweonly know probabilities associated with a standard normal random variable (0 1) We solve the problem by standardizing as follows: µ 2 Pr ( 5) = Pr 5 2 µ =Pr 3 = Standardizing a random variable is often done in the construction of test statistics. For example, the so-called t-statistic or t-ratio used for testing simple hypotheses on coefficients is constructed by the standardization process. A non-standard random variable with mean and variance 2 can be created from a standard random variable via the linear transformation: = + (1.22) This result is useful for modeling purposes as illustrated in the next example. Example 43 Quantile of general normal random variable

33 1.1 RANDOM VARIABLES 33 Let ( 2 ) Quantiles of can be conveniently computed using = + (1.23) where (0 1) and is the 100% quantile of a standard normal random variable. This formula is derived as follows. By the definition and (1.22) =Pr( )=Pr( + + )=Pr( + ) which implies (1.23). Example 44 Constant expected return model for asset returns Let denote the monthly continuously compounded return on an asset, and assume that ( 2 ). Then can be expressed as = + (0 1) The random variable can be interpreted as representing the random news arriving in a given month that makes the observed return differ from its expected value Thefactthat has mean zero means that news, on average, is neutral. The value of represents the typical size of a news shock. The bigger is the larger is the impact of a news shock and vice-versa Value at Risk: An Introduction As an example of working with linear functions of a normal random variable, and to illustrate the concept of Value-at-Risk (VaR), consider an investment of \$10,000 in Microsoft stock over the next month. Let denote the monthly simple return on Microsoft stock and assume that (0 05 (0 10) 2 ). That is, [ ] = =0 05 and var( ) = 2 =(0 10)2 Let 0 denote the investmentvalueatthebeginningofthemonthand 1 denote the investment value at the end of the month. In this example, 0 = \$ Consider the following questions: (i) What is the probability distribution of end of month wealth, 1? (ii) What is the probability that end of month wealth is less than \$9 000 and what must the return on Microsoft be for this to happen?

34 34 CHAPTER 1 REVIEW OF RANDOM VARIABLES (iii) What is the loss in dollars that would occur if the return on Microsoft stock is equal to its 5% quantile, 05? That is, what is the monthly 5% VaR on the \$ investment in Microsoft? To answer (i), note that end of month wealth, 1 is related to initial wealth 0 and the return on Microsoft stock via the linear function 1 = 0 (1 + ) = =\$ \$ Using the properties of linear functions of a random variable we have and [ 1 ]= [ ] =\$ \$10 000(0 05) = \$ var( 1 )=( 0 ) 2 var( ) = (\$10 000) 2 (0 10) 2 sd( 1 )=(\$10 000)(0 10) = \$1 000 Further, since is assumed to be normally distributed it follows that 1 is normally distributed: 1 (\$ (\$1 000) 2 ) To answer (ii), we use the above normal distribution for 1 to get Pr( 1 \$9 000) = To find the return that produces end of month wealth of \$9 000 or a loss of \$ \$9 000 = \$1 000 we solve \$9 000 \$ = = 0 10 \$ If the monthly return on Microsoft is 10% or less, then end of month wealth will be \$9 000 or less. Notice that = 0 10 is the 6 7% quantile of the distribution of : Pr( 0 10) = Question (iii) can be answered in two equivalent ways. First, we use (0 05 (0 10) 2 ) and solve for the 5% quantile of Microsoft Stock using (1.23) 5 : Pr( 05) = = + 05 = ( 1 645) = Using R, 05 = (0 05) = 1 645

35 1.1 RANDOM VARIABLES 35 That is, with 5% probability the return on Microsoft stock is 11 4% or less. Now, if the return on Microsoft stock is 11 4% thelossininvestment value is \$ (0 114) = \$1 144 Hence, \$1 144 is the 5% VaR over the nextmonthonthe\$ investment in Microsoft stock. For the second method, use 1 ~ (\$ (\$1 000) 2 ) and solve for the 5% quantile of end of month wealth directly: Pr( )= =\$8 856 This corresponds to a loss of investment value of \$ \$8 856 = \$1 144 Hence, if 0 represents the initial wealth and 1 05 is the 5% quantile of the distribution of 1 then the 5% VaR is 5% VaR = In general if 0 represents the initial wealth in dollars and is the 100% quantile of distribution of the simple return then the 100% VaR is defined as VaR = 0 (1.24) In words, VaR represents the dollar loss that could occur with probability By convention, it is reported as a positive number (hence the use of the absolute value function). Value-at-Risk Calculations for Continuously Compounded Returns The above calculations illustrate how to calculate value-at-risk using the normal distribution for simple returns. However, as argued in Example 31, the normal distribution may not be appropriate for characterizing the distribution of simple returns and may be more appropriate for characterizing the distribution of continuously compounded returns. Let denote the simple monthly return, let =ln(1+ ) denote the continuously compounded return and assume that ( 2 ) The 100% monthly VaR on an investment of \$ 0 is computed using the following steps: 1. Compute the 100% quantile,, from the normal distribution for the continuously compounded return : = + where is the 100% quantile of the standard normal distribution

36 36 CHAPTER 1 REVIEW OF RANDOM VARIABLES 2. Convert the continuously compounded return quantile,,toasimple return quantile using the transformation = 1 3. Compute VaR using the simple return quantile (1.24). Example 45 Computing VaR from simple and continuously compounded returns using R Let denote the simple monthly return on Microsoft stock and assume that (0 05 (0 10) 2 ) Consider an initial investment of 0 =\$ To compute the 1% and 5% VaR values over the next month use >mu.r=0.05 >sd.r=0.10 >w0=10000 >q.01.r=mu.r+sd.r*qnorm(0.01) >q.05.r=mu.r+sd.r*qnorm(0.05) > VaR.01 = abs(q.01.r*w0) > VaR.05 = abs(q.05.r*w0) > VaR.01 [1] 1826 > VaR.05 [1] 1145 Hence with 1% and 5% probability the loss over the next month is at least \$1,826 and \$1,145, respectively. Let denote the continuously compounded return on Microsoft stock and assume that (0 05 (0 10) 2 ) To compute the 1% and 5% VaR values over the next month use >mu.r=0.05 >sd.r=0.10 >q.01.r=exp(mu.r+sd.r*qnorm(0.01)) - 1 >q.05.r=exp(mu.r+sd.r*qnorm(0.05)) - 1 > VaR.01 = abs(q.01.r*w0) > VaR.05 = abs(q.05.r*w0) > VaR.01

37 1.2 BIVARIATE DISTRIBUTIONS 37 [1] 1669 > VaR.05 [1] 1082 Notice that when 1+ = has a log-normal distribution, the 1% and 5% VaR values (losses) are slightly smaller than when is normally distributed. This is due to the positive skewness of the log-normal distribution. 1.2 Bivariate Distributions So far we have only considered probability distributions for a single random variable. Inmanysituationswewanttobeabletocharacterizetheprobabilistic behavior of two or more random variables simultaneously. In this section, we discuss bivariate distributions Discrete Random Variables Let and be discrete random variables with sample spaces and respectively. The likelihood that and takes values in the joint sample space = is determined by the joint probability distribution ( ) =Pr( = = ) The function ( ) satisfies (i) ( ) 0 for ; (ii) ( ) =0for ; (iii) P ( ) = P P ( ) =1 Example 46 Bivariate discrete distribution for stock returns Let denote the monthly return (in percent) on Microsoft stock and let denote the monthly return on Apple stock. For simplicity suppose that the sample spaces for and are = { } and = {0 1} so that the random variables and are discrete. The joint sample space is the two dimensional grid = {(0 0) (0 1) (1 0) (1 1) (2 0) (2 1) (3 0) (3 1)} Table 1.3 illustrates the joint distribution for and From the table, (0 0) = Pr( =0 =0)=1 8 Notice that sum of all the entries in the table sum to unity. The bivariate distribution is illustrated graphically in Figure 1.11 as a 3-dimensional barchart.

38 38 CHAPTER 1 REVIEW OF RANDOM VARIABLES % 0 1 Pr( ) 0 1/8 0 1/8 1 2/8 1/8 3/8 2 1/8 2/8 3/ /8 1/8 Pr( ) 4/8 4/8 1 Table 1.3: prices. Discrete bivariate distribution for Microsoft and Apple stock Marginal Distributions The joint probability distribution tells the probability of and occuring together. What if we only want to know about the probability of occurring, or the probability of occuring? Example 47 Find Pr( =0)and Pr( =1)from joint distribution Consider the joint distribution in Table 1.3. What is Pr( =0)regardless of the value of? Now can occur if =0or if =1and since these twoeventsaremutuallyexclusivewehavethatpr( =0)=Pr( =0 = 0) + Pr( =0 =1)=0+1 8 =1 8 Notice that this probability is equal to the horizontal (row) sum of the probabilities in the table at =0 We can find Pr( =1)in a similar fashion: Pr( =1)=Pr( =0 =1)+Pr( = 1 =1)+Pr( =2 =1)+Pr( =3 =1)= =4 8 This probability is the vertical (column) sum of the probabilities in the table at =1 The probability Pr( = ) is called the marginal probability of and is given by Pr( = ) = X Pr( = = ) (1.25) Similarly, the marginal probability of = is given by Pr( = ) = X Pr( = = ) (1.26)

39 1.2 BIVARIATE DISTRIBUTIONS 39 Bivariate pdf p(x,y) x y Figure 1.11: Discrete bivariate distribution. Example 48 Marginal probabilities of discrete bivariate distribution The marginal probabilities of = are given in the last column of Table 1.3, and the marginal probabilities of = are given in the last row of Table 1.3. Notice that these probabilities sum to 1. For future reference we note that [ ] =3 2 var( ) =3 4 [ ]=1 2 and var( )=1 4. Conditional Distributions For random variables in Table 1.3, suppose we know that the random variable takes on the value =0 Howdoesthisknowledgeaffect the likelihood that takes on the values or 3? For example, what is the probability that =0given that we know =0?To find this probability, we use Bayes law and compute the conditional probability Pr( =0 =0)= Pr( =0 =0) Pr( =0) = =1 4 The notation Pr( =0 =0)is read as the probability that =0given that =0. Notice that Pr( =0 =0)=1 4 Pr( =0)=1 8

40 40 CHAPTER 1 REVIEW OF RANDOM VARIABLES Hence, knowledge that =0increases the likelihood that =0 Clearly, depends on Now suppose that we know that =0. How does this knowledge affect the probability that =0?To find out we compute Pr( =0 =0)= Pr( =0 =0) Pr( =0) = =1 Notice that Pr( =0 =0)=1 Pr( =0)=1 2. That is, knowledge that =0makes it certain that =0 In general, the conditional probability that = given that = (provided Pr( = ) 6= 0)is Pr( = = ) = Pr( = = ) (1.27) Pr( = ) and the conditional probability that = given that = (provided Pr( = ) 6= 0)is Pr( = = ) = Pr( = = ) (1.28) Pr( = ) Example 49 Conditional distributions For the bivariate distribution in Table 1.3, the conditional probabilities along with marginal probabilities are summarized in Tables 1.4 and 1.5. Notice that the marginal distribution of is centered at =3 2 whereas the conditional distribution of =0is centered at =1and the conditional distribution of =1is centered at =2 Conditional Expectation and Conditional Variance Just as we defined shape characteristics of the marginal distributions of and we can also define shape characteristics of the conditional distributions of = and = Themostimportantshapecharacteristicsarethe conditional expectation (conditional mean)andtheconditional variance. The conditional mean of = is denoted by = = [ = ] and the

41 1.2 BIVARIATE DISTRIBUTIONS 41 x Pr( = ) Pr( =0) Pr( =1) 0 1/8 2/ /8 4/8 2/8 2 3/8 2/8 4/8 3 1/8 0 2/8 Table 1.4: Conditional probability distribution of X from bivariate discrete distribution. y Pr( = ) Pr( =0) Pr( =1) Pr( =2) Pr( =3) 0 1/2 1 2/3 1/ /2 0 1/3 2/3 1 Table 1.5: Conditional distribution of Y from bivariate discrete distribution. conditional mean of = is denoted by = = [ = ]. These means are computed as = = = [ = ] = X Pr( = = ) (1.29) = [ = ] = X Pr( = = ) (1.30) Similarly, the conditional variance of = is denoted by 2 = =var( = ) and the conditional variance of = is denoted by 2 = =var( = ) These variances are computed as 2 = 2 = =var( = ) = X ( = ) 2 Pr( = = ) (1.31) =var( = ) = X ( = ) 2 Pr( = = ) (1.32) Example 50 Compute conditional expectation and conditional variance

42 42 CHAPTER 1 REVIEW OF RANDOM VARIABLES For the random variables in Table 1.3, we have the following conditional moments for : [ =0]= =1 [ =1]= =2 var( =0)=(0 1) (1 1) (2 1) (3 1) 2 0=1 2 var( =1)=(0 2) 2 0+(1 2) (2 2) (3 2) =1 2 Compare these values to [ ] =3 2 and var( ) =3 4 Notice that as increases, [ = ] increases. For similar calculations gives [ =0]=0 [ =1]=1 3 [ =2]=2 3 [ =3]=1 var( =0)=0 var( =1)= var( =2)= var( =3)=0 Compare these values to [ ]=1 2 and var( )=1 4 Notice that as increases [ = ] increases. Conditional Expectation and the Regression Function Consider the problem of predicting the value giventhatweknow = A natural predictor to use is the conditional expectation [ = ] In this prediction context, the conditional expectation [ = ] is called the regression function. Thegraphwith [ = ] on the vertical axis and on the horizontal axis gives the regression line. The relationship between and the regression function may expressed using the trivial identity = [ = ]+ [ = ] = [ = ]+ (1.33) where = [ ] is called the regression error. Example 51 Regression line for bivariate discrete distribution For the random variables in Table 1.3, the regression line is plotted in Figure Notice that there is a linear relationship between [ = ] and When such a linear relationship exists we call the regression function a linear regression. Linearity of the regression function, however, is not guaranteed. It may be the case that there is a non-linear (e.g., quadratic) relationship between [ = ] and. In this case, we call the regression function a non-linear regression.

43 1.2 BIVARIATE DISTRIBUTIONS 43 E[Y X=x] x Figure 1.12: Regression function [ = ] from discrete bivariate distribution. Law of Total Expectations For the random variables in Table 1.3, notice that and [ ] = [ =0] Pr( =0)+ [ =1] Pr( =1) = =3 2 [ ]= [ =0] Pr( =0)+ [ =1] Pr( =1) + [ =2] Pr( =2)+ [ =3] Pr( =3)=1 2 This result is known as the law of total expectations. In general, for two random variables and (discrete or continuous) we have [ ] = [ [ ]] (1.34) [ ]= [ [ ]]

44 44 CHAPTER 1 REVIEW OF RANDOM VARIABLES where the first expectation is taken with respect to and the second expectation is taken with respect to Bivariate Distributions for Continuous Random Variables Let and be continuous random variables defined over the real line. We characterize the joint probability distribution of and using the joint probability function (pdf) ( ) such that ( ) 0 and Z Z ( ) =1 The three-dimensional plot of the joint probability distribution gives a probability surface whose total volume is unity. To compute joint probabilities of 1 2 and 1 2 we need to find the volume under the probability surface over the grid where the intervals [ 1 2 ] and [ 1 2 ] overlap. Findingthisvolumerequriessolvingthedoubleintegral Pr( )= Z 2 Z 2 Example 52 Bivariate standard normal distribution 1 1 ( ) A standard bivariate normal pdf for and has the form ( ) = ( 2 + 2) (1.35) and has the shape of a symmetric bell (think Liberty Bell) centered at =0 and =0 To find Pr( ) we must solve Z 1 Z ( 2 + 2) which, unfortunately, does not have an analytical solution. Numerical approximationmethodsarerequiredtoevaluatetheaboveintegral.thefunction pmvnorm() in the R package mvtnorm can be used to evaluate areas under the bivariate standard normal surface. To compute Pr( ) use

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