2. Copyright © 2011 Pearson Education, Inc. Association between Random Variables Chapter 10

3. 10.1 Portfolios and Random Variables How should money be allocated among several stocks that form a portfolio?  Need to manipulate several random variables at once to understand portfolios  Since stocks tend to rise and fall together, random variables for these events must capture dependence Copyright © 2011 Pearson Education, Inc. 3 of 44

4. 10.1 Portfolios and Random Variables Two Random Variables  Suppose a day trader can buy stock in two companies, IBM and Microsoft, at $100 per share  X denotes the change in value of IBM  Y denotes the change in value of Microsoft Copyright © 2011 Pearson Education, Inc. 4 of 44

5. 10.1 Portfolios and Random Variables Probability Distribution for the Two Stocks Copyright © 2011 Pearson Education, Inc. 5 of 44

6. 10.1 Portfolios and Random Variables Comparisons and the Sharpe Ratio The day trader can invest $200 in  Two shares of IBM;  Two shares of Microsoft; or  One share of each Copyright © 2011 Pearson Education, Inc. 6 of 44

7. 10.1 Portfolios and Random Variables Which portfolio should she choose? Summary of the Two Single Stock Portfolios Copyright © 2011 Pearson Education, Inc. 7 of 44

8. 10.2 Joint Probability Distribution Find Sharpe Ratio for Two Stock Portfolio  Combines two different random variables (X and Y) that are not independent  Need joint probability distribution that gives probabilities for events of the form (X = x and Y = y) Copyright © 2011 Pearson Education, Inc. 8 of 44

9. 10.2 Joint Probability Distribution Joint Probability Distribution of X and Y Copyright © 2011 Pearson Education, Inc. 9 of 44

10. 10.2 Joint Probability Distribution Independent Random Variables Two random variables are independent if (and only if) the joint probability distribution is the product of the marginal distributions. p(x,y) = p(x) p(y) for all x,y Copyright © 2011 Pearson Education, Inc. 10 of 44

11. 10.2 Joint Probability Distribution Multiplication Rule The expected value of a product of independent random variables is the product of their expected values. E(XY) = E(X)E(Y) Copyright © 2011 Pearson Education, Inc. 11 of 44

12. 4M Example 10.1: EXCHANGE RATES Motivation A firm’s sales in Europe average 10 million € each month. The current exchange rate is 1.40$/€ but it fluctuates. What should this firm expect for the dollar value of European sales next month? Copyright © 2011 Pearson Education, Inc. 12 of 44

13. 4M Example 10.1: EXCHANGE RATES Motivation Fluctuating Exchange Rates Copyright © 2011 Pearson Education, Inc. 13 of 44

14. 4M Example 10.1: EXCHANGE RATES Method Identify three random variables: S = sales next month in €; R = exchange rate next month; and D = value of sales in $. These are related by D = S R. Find E(D). Copyright © 2011 Pearson Education, Inc. 14 of 44

15. 4M Example 10.1: EXCHANGE RATES Mechanics Assume E(R) = 1.40$/€ and independence between S and R. E(D) = E(R S) = E(S) E(R) = € 10,000,000 1.4 = $14 million Copyright © 2011 Pearson Education, Inc. 15 of 44

16. 4M Example 10.1: EXCHANGE RATES Message European sales for next month convert to $14 million, on average. We assume that sales next month are, on average, the same as in the past for this firm and that sales and exchange rate are independent. Copyright © 2011 Pearson Education, Inc. 16 of 44

17. 10.2 Joint Probability Distribution Dependent Random Variables  Joint probability table shows changes in values of IBM and Microsoft (X and Y) are dependent  The dependence between them is positive Copyright © 2011 Pearson Education, Inc. 17 of 44

18. 10.3 Sums of Random Variables Addition Rule for Expected Value of a Sum The expected value of a sum of random variables is the sum of their expected values. E(X + Y) = E(X) + E(Y) Copyright © 2011 Pearson Education, Inc. 18 of 44

19. 10.3 Sums of Random Variables Addition Rule for Expected Value of a Sum The mean of the portfolio that mixes IBM and Microsoft is E(X + Y) = µ x + µ Y = 0.10 + 0.12 = $ 0.22 Copyright © 2011 Pearson Education, Inc. 19 of 44

20. 10.3 Sums of Random Variables Variance of a Sum of Random Variables The variance of a sum of random variables is not necessarily the sum of the variances. The variance for the portfolio that mixes IBM and Microsoft is larger than the sum: Var(X + Y) = 14.64 $ 2 Copyright © 2011 Pearson Education, Inc. 20 of 44

21. 10.3 Sums of Random Variables Sharpe Ratio for Mixed Portfolio Copyright © 2011 Pearson Education, Inc. 21 of 44

22. 10.3 Sums of Random Variables Summary of Sharpe Ratios (Shows Advantage of Diversifying) Copyright © 2011 Pearson Education, Inc. 22 of 44

23. 10.4 Dependence Between Random Variables Covariance The covariance between random variables is the expected value of the product of deviations from the means. Cov(X,Y) = E((X - µ X ) (Y - µ Y )) Copyright © 2011 Pearson Education, Inc. 23 of 44

24. 10.4 Dependence Between Random Variables Positive Dependence Between X and Y Copyright © 2011 Pearson Education, Inc. 24 of 44

25. 10.4 Dependence Between Random Variables Covariance and Sums The variance of the sum of two random variables is the sum of their variances plus twice their covariance. Var(X + Y) = Var(X) + Var(Y) + 2Cov(X,Y) Copyright © 2011 Pearson Education, Inc. 25 of 44

26. 10.4 Dependence Between Random Variables Using the Addition Rule for Variances We get the following for the mixed portfolio: Copyright © 2011 Pearson Education, Inc. 26 of 44

27. 10.4 Dependence Between Random Variables Correlation The correlation between two random variables is the covariance divided by the product of standard deviations. Corr(X,Y) = Cov(X,Y)/σ x σ Y Copyright © 2011 Pearson Education, Inc. 27 of 44

28. 10.4 Dependence Between Random Variables Correlation  Denoted by the parameter ρ (“rho”)  Is always between -1 and 1  For the mixed portfolio, ρ = 0.43 Copyright © 2011 Pearson Education, Inc. 28 of 44

29. 10.4 Dependence Between Random Variables Joint Distribution with ρ = -1 Copyright © 2011 Pearson Education, Inc. 29 of 44

30. 10.4 Dependence Between Random Variables Joint Distribution with ρ = 1 Copyright © 2011 Pearson Education, Inc. 30 of 44

31. 10.4 Dependence Between Random Variables Covariance, Correlation and Independence  A correlation of zero does not necessarily imply independence  Independence does imply that the covariance and correlation are zero Copyright © 2011 Pearson Education, Inc. 31 of 44

32. 10.4 Dependence Between Random Variables Addition Rule for Variances of Independent Random Variables The variance of the sum of independent random variables is the sum of their variances. Var(X + Y) = Var(X) + Var(Y) Copyright © 2011 Pearson Education, Inc. 32 of 44

33. 10.5 IID Random Variables Definition  Random variables that are independent of each other and share a common probability distribution are said to be independent and identically distributed.  iid for short Copyright © 2011 Pearson Education, Inc. 33 of 44

34. 10.5 IID Random Variables Addition Rule for iid Random Variables If n random variables (X 1, X 2, …, X n ) are iid with mean µ x and standard deviation σ x, E(X 1 + X 2 +…+ X n ) = nµ x Var(X 1 + X 2 +…+ X n ) = nσ x 2 SD(X 1 + X 2 +…+ X n ) = σ x Copyright © 2011 Pearson Education, Inc. 34 of 44

35. 10.5 IID Random Variables IID Data Strong link between iid random variables and data with no pattern (e.g., IBM stock value changes) Copyright © 2011 Pearson Education, Inc. 35 of 44

36. 10.6 Weighted Sums Addition Rule for Weighted Sums The expected value of a weighted sum of random variables is the weighted sum of the expected values. E(aX + bY + c) = aE(X) + bE(Y) + c Copyright © 2011 Pearson Education, Inc. 36 of 44

37. 10.6 Weighted Sums Addition Rule for Weighted Sums The variance of a weighted sum of random variables is Var(aX + bY + c) = a 2 Var(X) + b 2 Var(Y) + 2abCov(X,Y) Copyright © 2011 Pearson Education, Inc. 37 of 44

38. 4M Example 10.2: CONSTRUCTION ESTIMATES Motivation Adding an addition to a home typically takes two carpenters working 240 hours with a standard deviation of 40 hours. Electrical work takes an average of 12 hours with standard deviation 4 hours. Carpenters charge $45/hour and electricians charge $80/hour. The amount of both types of labor could vary with ρ =0.5. What is the total expected labor cost? Copyright © 2011 Pearson Education, Inc. 38 of 44

39. 4M Example 10.2: CONSTRUCTION ESTIMATES Method Identify three random variables: X = number of carpentry hours; Y = number of electrician hours; and T = total costs ($). These are related by T = 45X + 80Y. Copyright © 2011 Pearson Education, Inc. 39 of 44

40. 4M Example 10.2: CONSTRUCTION ESTIMATES Mechanics: Find E(T) Using Addition Rule for Weighted Sums Copyright © 2011 Pearson Education, Inc. 40 of 44

41. 4M Example 10.2: CONSTRUCTION ESTIMATES Mechanics: Find Var(T) Using the Addition Rule for Weighted Sums Copyright © 2011 Pearson Education, Inc. 41 of 44

42. 4M Example 10.2: CONSTRUCTION ESTIMATES Message The expected total cost for labor is around $12,000 with a standard deviation of about $2,000. Copyright © 2011 Pearson Education, Inc. 42 of 44

43. Best Practices  Consider the possibility of dependence.  Only add variances for random variables that are uncorrelated.  Use several random variables to capture different features of a problem.  Use new symbols for each random variable. Copyright © 2011 Pearson Education, Inc. 43 of 44

44. Pitfalls  Do not think that uncorrelated random variables are independent.  Don’t forget the covariance when finding the variance of a sum.  Never add standard deviations of random variables.  Don’t mistake Var(X – Y) for Var(X) – Var(Y). Copyright © 2011 Pearson Education, Inc. 44 of 44