1. 1 MF-852 Financial Econometrics Lecture 3 Review of Probability Roy J. Epstein Fall 2003

2. 2 So Far We Have Seen That… Excel does all of our matrix number crunching. We can solve basic optimization (maximize, minimize) problems with constraints in Excel. We can find the  coefficients in the linear regression model (that is, predict y in terms of the x’s and an error term): y =  0 +  1 x 1 +  2 x 2 + … +  n x n + e Write the equation as y = X  + e. Solution is  = (XX) –1 Xy (Excel does this automatically with regression function in Tools-Data Analysis)

3. 3 Probability and Statistics We can always calculate the regression coefficients. But how reliable are they? Remember last time that more bedrooms caused lower prices in the regression! Probability and statistics are used to determine reliability of a data analysis.

4. 4 Sample Spaces and Events The sample space S defines the possible outcomes of an experiment. Coin flip: the sample space has two outcomes, heads (H) and tails (T). S = {H, T} Any given collection of outcomes in the sample space constitutes a possible event E. H is an event.

5. 5 Sample Spaces, cont. Sample spaces can be large. 3 coin flips: S = {HHH, HTH, HHT, HTT, THH, TTH, THT, TTT} Events can be complex. 2 heads is an event in S E = {HTH, HHT, THH}.

6. 6 Probability With the 3-coin flip, S has eight outcomes. E = {HTH, HHT, THH} therefore has probability 3/8. P(E) = 0.375.

7. 7 Independent Events Consider E 1 and E 2 defined on S. P(E 1 ) = p 1, P(E 2 ) = p 2 Probability of E 2 given that E 1 has occurred is written P(E 2 |E 1 ). Called conditional probability. If P(E 2 |E 1 ) = P(E 2 ) then E 2 and E 1 are independent events.

8. 8 Independence When two events A and B are independent, then knowledge that A occurred (or will occur) does not provide information about whether B occurred (or will occur).

9. 9 Example of Independence Consider the 3-coin flip. E 1 : first two coins are heads, P(E 1 )=2/8 E 2 : last coin is a head, P(E 2 )=4/8 E 3 : all three coins are heads, P(E 3 )=1/8 Are E 2 and E 1 independent? Are E 3 and E 1 independent?

10. 10 Example (cont.) Given that E 1 has occurred, the new sample space S = {HHH, HHT}. P(E 2 ) given E 1 is written P(E 2 |E 1 ). P(E 2 |E 1 ) = ½ P(E 2 ) = 4/8 = 1/2 Probability of E 2 doesn’t change E 2 and E 1 are independent. What about E 3 ? P(E 3 |E 1 ) = ½ P(E 3 ) = 1/8 Probabilities do change E 3 and E 1 are dependent (not independent).

11. 11 Independence and Probability of Intersection of Events Intersection: joint occurrence of E 1 and E 2 defined on S. Written as E 1  E 2 If P(E 1  E 2 ) = P(E 1 ) P(E 2 ) then the events are independent.

12. 12 Conditional Probability Restated P(E 2 |E 1 ) = P(E 2  E 1 ) / P(E 1 ) P(E 1 |E 2 ) = P(E 1  E 2 ) / P(E 2 ) If E 1 and E 2 are independent then P(E 1  E 2 ) = P(E 1 ) P(E 2 ) so that P(E 2 |E 1 ) = P(E 2 ) P(E 1 |E 2 ) = P(E 1 )

13. 13 Multiplication Test for Independence E 1  E 2 = HHH P(HHH) = 1/8 P(E 1 )P(E 2 ) = (2/8)(4/8) = 1/8 Events are independent by the test E 1  E 3 = HHH P(HHH) = 1/8 P(E 1 )P(E 3 ) = (2/8)(1/8) = 1/32 Events are not independent

14. 14 Probability Examples: Consumer Behavior Do you enjoy shopping for clothes? Survey of 500 MBA students revealed: YesNo Males136104 Females22436

15. 15 Example: Consumer Behavior What is probability that a respondent chosen at random is: A male? Enjoys shopping for clothes? Enjoys shopping for clothes, given being female? Is male, given does not enjoy shopping for clothes? Is male that enjoys shopping or female that does not enjoy shopping?

16. 16 Example: Consumer Behavior Is enjoyment of shopping for clothes independent of gender? Use multiplication test.

17. 17 Independence: Intuition When 2 events are independent, then information about one event provides no information about the other. Essential concept in building a statistical model. Joint probabilities can be calculated by simple multiplication. If any unused information is independent of the problem under study then the model is “efficient,” i.e., makes best use of the data.

18. 18 Random Variables A random variable is a function defined on the sample space that summarizes events of interest. 3-coin flip: the number of heads in the 3 flips is a random variable. The random variable takes on different values, each with a probability determined by the underlying sample space.

19. 19 Discrete Distributions 3-coin flip, random variable z = number of heads Four possible values: 0, 1, 2, 3 Distribution function f(z) gives probability of each z. zf(z) 01/8 13/8 23/8 31/8

20. 20 Discrete Distributions Everyone in class should now flip a coin 3 times. Let’s construct an empirical frequency distribution for the number of heads!

21. 21 Discrete Distributions Note that for a probability distribution: 0  f(z i )  1 (negative probability and probability greater than 1 makes no sense) ∑ f(z i ) = 1 (all the different outcomes must sum to 1 in probability)

22. 22 Expected Value Let z be a random variable with distribution f(z). The expected value of z is denoted E(z) or  z or just  if the context is clear. Also called mean value or the mean Expected value = ∑ zf(z). weighted average of z, where the probabilities are the weights.

23. 23 Expected Value (cont.) We can calculate the expected value of the number of heads in the 3-coin flip. E(z) = 0(1/8) + 1(3/8) + 2(3/8) +3(1/8) = 12/8 = 1.5 “On average,” we would get 1.5 heads.

24. 24 Properties of Expected Value Linearity (k is a scalar): E(kz) = kE(z) E(z + k) = E(z) + k E(z 1 + z 2 ) = E(z 1 ) + E(z 2 ) Example: suppose k=2 and z is the number of heads in the 3 coin flip. E(2z) = 2E(z) = 3 E(z + 2) = E(z) + 2 = 1.5 + 2 = 3.5

25. 25 Properties of Expected Value Average deviation of a random variable from its expected value is zero: E(z –  z ) = ∑ zf(z) – ∑  z f(z) =  z –  z ∑ f(z) =  z –  z = 0 Since “on average” a random variable equals its expected value, the average deviation from the mean is 0!

26. 26 A Card Game with Monte Hall We will play in class.

27. 27 Variance Variance is the expected value of the square of the deviation of a random variable from its mean. Var(z) =  2 = E[(z –  z ) 2 ] = ∑( z 2 – 2  z z + ∑  z 2 )f(z) = ∑( z 2 )f(z) – 2  z ∑ zf(z) +  z 2 = ∑( z 2 )f(z) –  z 2

28. 28 Variance (cont.) Variance of the number of heads in the 3- coin toss: z 2 f(z) 01/8 13/8 43/8 91/8 E(z 2 ) = 0(1/8) + 1(3/8) + 4(3/8) + 9(1/8) = 24/8 = 3 Var(z) = E(z 2 ) –  z 2 = 3 – (1.5) 2 = 0.75

29. 29 Properties of Variance Var(kz) = k 2 Var(z) Var(z + k) = Var(z) Var(z) = 0  z is a constant Example: suppose k=2 and z is the number of heads in the 3 coin flip. Var(2z) = 4(.75) = 3 Var(z + 2) =.75

30. 30 Standard Deviation Standard deviation (  ) is the square root of the variance. It is used throughout statistical analysis. Related to mean absolute deviation but more convenient. If z is a random variable, what is the standard deviation of kz?

31. 31 Joint Distributions f(z,y) = joint distribution of random variables Gives probability of joint occurrences of the random variables. Define 2 random variables from the 3- coin flip: z = number of heads y = number of changes in sequence (e.g., HHT is one change in sequence, HTH is two changes, etc.)

32. 32 Joint Distributions S = {HHH, HTH, HHT, HTT, THH, TTH, THT, TTT} y 012p(z) z 01/8001/8 102/81/83/8 202/81/83/8 31/8001/8 p(y)2/84/82/8 ∑ f(z,y)=1

33. 33 Covariance Random variables y and z have positive covariance if: On average, when y is above (below) its mean then z is also above (below) its mean. Negative covariance: On average, when y is above (below) its mean then z is below (above) its mean.

34. 34 Calculation of Covariance Cov(y,z) =  yz = E[(y –  y )(z –  z )] = ∑ (y –  y )(z –  z )f(y,z) Assume f(y,z) is y 010p(z) z 00.4.4 10.2.2.4 20.20.2 p(y).4.6 ∑ f(z,y)=1

35. 35 Covariance and Dependence E(z) = 0(.4) + 10(.4) + 20(.2) = 8 E(y) = 0(.4) + 10(.6) = 6 Cov(y,z) = (0–6)(0–8)(0) + (0–6)(10–8)(.2) + (0–6)(20–8)(.2) + (10–6)(0–8)(.4) + (10–6)(10–8)(.2) + (10–6)(20–8)(0) = –2.4 – 14.4 – 12.8 + 1.6 = –28 Covariance means z and y are NOT independent. (Check with multiplication test)

36. 36 Dependence and Covariance Cov(y,z) =  yz = E[(y –  y )(z –  z )] = ∑ (y –  y )(z –  z )f(y,z) From 3-coin flip:  y = 1,  z = 1.5 Cov(y,z) = (0–1)(0–1.5)(1/8) + (0–1)(1–1.5)(0) + (0–1)(2–1.5)(0) + (0–1)(3–1.5)(1/8) + (1–1)(0– 1.5)(0) + (1–1)(1–1.5)(2/8) + (1–1)(2–1.5)(2/8) + (1–1)(3–1.5)(0) + (2–1)(0–1.5)(0) + (2–1)(1– 1.5)(1/8) + (2–1)(2–1.5)(1/8) + (2–1)(3–1.5)(0) = 1.5/8 – 1.5/8 –.5/8 +.5/8 = 0 (But check multiplication test for independence)

37. 37 Mean and Variance of a Linear Combination Suppose w =  y +  z y and z are random variables  and  are scalars (constants) E(w) =  E(y) +  E(z) Var(w) =  2 Var(y) +  2 Var(z) + 2  Cov(y,z)

38. 38 Standardizing Transformation Suppose z is a random variable with E(z) = , Var(z) =  2 Define new random variable E(w) = 0, Var(w) = 1 True regardless of the distribution of z (provided the mean and variance exist)

39. 39 Covariance Summary Covariance means that random variables are not independent. One variable can help “predict” the other. Independence implies zero covariance. BUT zero covariance does not guarantee independence.

40. 40 Correlation Coefficient Alternative to covariance: Corr(y,z) =  yz =  yz / (  y  z ) Invariant to units of measurement for the random variables. The correlation coefficient is bounded between –1 and +1. Often more convenient than covariance for measuring “how closely” variables move together.

41. 41 Calculation of Correlation Coefficient Assume f(y,z) is y 010p(z) z 00.4.4 10.2.2.4 20.20.2 p(y).4.6 ∑ f(z,y)=1

42. 42 Correlation and Covariance E(z) = 0(.4) + 10(.4) + 20(.2) = 8 E(y) = 0(.4) + 10(.6) = 6  y = [(0–6) 2 (.4) + (10–6) 2 (.6)] 1/2 = 4.90  z = [(0–8) 2 (.4) + (10–8) 2 (.4) + (20– 8) 2 (.2)] 1/2 = 7.48  yz = –28 / (4.90 x 7.48) = –0.764

43. 43 Correlation, Raw Data

44. 44 Correlation (Levels) = 0.83

45. 45 Correlation (% Change) = –0.12