Session 9a

Decision Models -- Prof. Juran2 Overview Finance Simulation Models Forecasting –Retirement Planning –Butterfly Strategy Risk Management –Introduction to VaR –Currency Risk Using Historical Data in Simulations –Parametric Approach –Resampling Approach

Decision Models -- Prof. Juran3 Example 1: Retirement Planning Amanda has 30 years to save for her retirement. At the beginning of each year, she puts $5000 into her retirement account. At any point in time, all of Amanda's retirement funds are tied up in the stock market. Suppose the annual return on stocks follows a normal distribution with mean 12% and standard deviation 25%. What is the probability that at the end of 30 years, Amanda will have reached her goal of having $1,000,000 for retirement? Assume that if Amanda reaches her goal before 30 years, she will stop investing.

Decision Models -- Prof. Juran4

5 The annual investment activities (columns A-D, beginning in row 5) actually extend down to row 35, to include 30 years of simulated returns. The range C6:C35 will be random numbers, generated by Crystal Ball. We could track Amanda’s simulated investment performance either with cell F5 (simply =D35, the final amount in Amanda’s retirement account), or with F4 (the maximum amount over 30 years). Using F4 allows us to assume that she would stop investing if she ever reached $1,000,000 at any time during the 30 years, which is the assumption given in the problem statement. Cell H1 is either 1 (she made it to $1 million) or 0 (she didn’t). Over many trials, the average of this cell will be out estimate of the probability that Amanda does accumulate $1 million. This will be a Crystal Ball forecast cell.

Decision Models -- Prof. Juran6

7

8

9

10

Decision Models -- Prof. Juran11

Decision Models -- Prof. Juran12 It looks like Amanda has about a 48% chance of meeting her goal of $1 million in 30 years.

Decision Models -- Prof. Juran13 Example 2: Butterfly The S&J index is a measure of overall equity value in the software publishing industry. Shares of a “tracking” mutual fund (a fund that tracks this index) are available from Avant Garde Investments, Inc. Shares in the mutual fund are currently available at a price of $605.

Decision Models -- Prof. Juran14 Avant Garde also sells 1-month call options on the S&J index, with current prices as follows: Strike Option Bid Price Option Ask Price 580 $25.54 $ $22.84 $ $20.33 $ $18.01 $ $15.79 $ $13.95 $ $12.09 $ $10.60 $10.70 (A call option gives its holder the right to purchase one share on the expiration date at the strike price. For example, if we buy one call option at the 600 strike price, and the S&J is at 620 on the expiration date, we can exercise the option and buy one share at 600 and immediately sell it for a $20 gross profit. The net profit would be $20.00– $15.89 = $4.11, which is a ($4.11 / $15.89) = 25.9% gain.)

Decision Models -- Prof. Juran15 We are considering investing $100,000 in the S&J index over the next month, based on our estimation that the S&J’s level one month from now is a log-normally distributed random variable with a mean of 605 and a one month standard deviation of 30. An analyst proposes that in addition to investing the $100,000 in the S&J index, we take some positions in call options. He suggests selling 200 options contracts (1 option contract is an option to purchase 100 shares) at the 605 strike price, and buying 100 option contracts each of the 600 and 610 strike prices. What do you think of this scheme? Does it have any advantage over simply investing all the money in the index? Assume that there are no transaction costs.

Decision Models -- Prof. Juran16

Decision Models -- Prof. Juran17 Put in quantities bought and sold, according to the analyst’s proposal

Decision Models -- Prof. Juran18 Figure out how much cash is going out, in D10:D17

Decision Models -- Prof. Juran19 Cell A5 will be an assumption; the ending price of the option in one month. Put cell references to A5 into H10:H17.

Decision Models -- Prof. Juran20 In I10:I17 enter a formula to calculate the payoff for options bought, as a function of the random ending price of the index.

Decision Models -- Prof. Juran21 Similarly, in J10:J17 enter a formula to calculate the payoff for options sold, as a function of the random ending price of the index.

Decision Models -- Prof. Juran22 In B19:B20, calculate how many shares of the index are being purchased.

Decision Models -- Prof. Juran23 In E10:E17, calculate the amount of cash coming back in at the end of the month.

Decision Models -- Prof. Juran24 In D2:F2, calculate the P/L from the index.

Decision Models -- Prof. Juran25 In D3:F3, calculate the P/L from the options.

Decision Models -- Prof. Juran26 In D4:F4, calculate the total P/L.

Decision Models -- Prof. Juran27 In F6 calculate the difference between the two strategies (with and without the options).

Decision Models -- Prof. Juran28

Decision Models -- Prof. Juran29

Decision Models -- Prof. Juran30

Decision Models -- Prof. Juran31

Decision Models -- Prof. Juran32

Decision Models -- Prof. Juran33 An old Excel trick: DataTable

Decision Models -- Prof. Juran34 Select A24:B55, then Data Table

Decision Models -- Prof. Juran35

Decision Models -- Prof. Juran36

Decision Models -- Prof. Juran37 3. Evaluation of Hedging Strategies It is July 1, 2002, and international entrepreneurs Clifford & Kearns (C&K) are concerned about volatility in the exchange rates between U.S. dollars and certain European currencies. C&K have incurred costs in dollars to develop, produce, and distribute merchandise to Norway, Switzerland, and Great Britain, for which they expect to realize revenues in 12 months.

Decision Models -- Prof. Juran38 Specifically, they expect to earn 1 million units each of British pounds, Swiss francs, and Norwegian kroner. Based on current exchange rates, this should result in $2,337,700 in revenue (see current rates below).

Decision Models -- Prof. Juran39 Unfortunately, it is possible that one or more of these currencies could devalue against the dollar in that one year, causing C&K to realize a smaller total revenue (in dollars) than expected. C&K has turned to their investment bank, Nuccio, Noto, and Rizzi (NNR) for advice. NNR has recommended buying 1.3 million 1-year Euro put options with a strike price of $0.98, for $ each. NNR claims that this hedging strategy will substantially decrease the risk of a large loss due to exchange rate fluctuations.

Decision Models -- Prof. Juran40 (a)Create a simulation model to study the “unhedged” distribution of revenue for C&K, using the historical exchange rate data in Exhibit 2. Make a histogram and report summary statistics. What is the 5% value at risk (VAR) for C&K’s revenue from these three countries over the next 12 months? What is the probability that C&K’s revenue will be less than $2,087,700 (i.e., a $250,000 loss or worse)? (b)Create a simulation model to study the “hedged” distribution of revenue for C&K. Make a histogram and report summary statistics with the policy recommended by NNR. What is the 5% VAR for C&K’s revenue from these three countries over the next 12 months? What is the probability that C&K’s revenue will be less than $2,087,700?

Decision Models -- Prof. Juran41

Decision Models -- Prof. Juran42

Decision Models -- Prof. Juran43 Converting prices into returns:

Decision Models -- Prof. Juran44 Here are summary statistics for each of the currencies’ returns against the dollar, including a t -test to see if the means are significantly different from zero (they are not) :

Decision Models -- Prof. Juran45

Decision Models -- Prof. Juran46 Distribution fitting: Checking to see which Crystal Ball distribution best fits the data (in this case the British pound’s return against the dollar).

Decision Models -- Prof. Juran47

Decision Models -- Prof. Juran48

Decision Models -- Prof. Juran49

Decision Models -- Prof. Juran50

Decision Models -- Prof. Juran51 It turns out that all four of our variables can be modeled reasonably well by normal distributions; normal is always either the best fit or the second best fit. We’ll use normal distributions with means of zero and standard deviations estimated from our sample data.

Decision Models -- Prof. Juran52

Decision Models -- Prof. Juran53 We start by creating the “January” cell for each currency. The Swiss franc:

Decision Models -- Prof. Juran54 The Norwegian kroner:

Decision Models -- Prof. Juran55 The British pound:

Decision Models -- Prof. Juran56

Decision Models -- Prof. Juran57 For more than a few correlated green cells, it’s more efficient to use the matrix view. You can specify bivariate correlations in the Define Assumption window.

Decision Models -- Prof. Juran58 Back inside the Swiss franc (after defining two other green cells):

Decision Models -- Prof. Juran59

Decision Models -- Prof. Juran60

Decision Models -- Prof. Juran61

Decision Models -- Prof. Juran62

Decision Models -- Prof. Juran63

Decision Models -- Prof. Juran64

Decision Models -- Prof. Juran65

Decision Models -- Prof. Juran66

Decision Models -- Prof. Juran67

Decision Models -- Prof. Juran68

Decision Models -- Prof. Juran69

Decision Models -- Prof. Juran70 VaR approach: Click the right grabber and then enter 95 in the certainty box – = ($296,500)

Decision Models -- Prof. Juran71 “Round dollar amount” approach: – = Chances of losing $250k or more = 1 – =

Decision Models -- Prof. Juran72

Decision Models -- Prof. Juran73

Decision Models -- Prof. Juran74 +Smaller standard deviation +Truncated lower tail −Lower expected value

Decision Models -- Prof. Juran75 +VaR is $196,800 (better than $296,500)

Decision Models -- Prof. Juran76 +Chance of $250k loss (better than )

Decision Models -- Prof. Juran77

Decision Models -- Prof. Juran78

Decision Models -- Prof. Juran79 The Parametric Approach “Fit” the data to some theoretical distribution (such as normal or exponential) and estimate the parameters appropriate to the distribution (such as mean and standard deviation for a normal distribution, or lambda for an exponential distribution). Advantage: Simplicity (a random variable can be described with a few parameters instead of all the data). Disadvantage: Need assurance that the theoretical distribution we choose is in fact a good “fit” to the data. This gives rise to a special kind of hypothesis test, called a goodness- of-fit test.

Decision Models -- Prof. Juran80 The Parametric Approach 1. find which theoretical distribution best fits each variable, 2. estimate the proper parameters for each, and 3. specify a correlation coefficient for the relationship between the two variables.

Decision Models -- Prof. Juran81

Decision Models -- Prof. Juran82

Decision Models -- Prof. Juran83

Decision Models -- Prof. Juran84

Decision Models -- Prof. Juran85

Decision Models -- Prof. Juran86

Decision Models -- Prof. Juran87

Decision Models -- Prof. Juran88

Decision Models -- Prof. Juran89

Decision Models -- Prof. Juran90

Decision Models -- Prof. Juran91

Decision Models -- Prof. Juran92

Decision Models -- Prof. Juran93

Decision Models -- Prof. Juran94

Decision Models -- Prof. Juran95

Decision Models -- Prof. Juran96 The Resampling Approach In this approach, we make no assumptions about any theoretical distributions that may or may not actually fit our data; we use the data themselves as the basis for our simulation. Advantages: Avoids the problem of Type II errors in the Chi-square test. Also spares us from dealing explicitly with correlation. Disadvantage: our model may have to include a large set of data (as opposed to the few parameters we used in the parametric approach).

Decision Models -- Prof. Juran97 Back to our example. Start the model with a spreadsheet similar to the parametric one. Notice the integers in column A.

Decision Models -- Prof. Juran98 Use the VLOOKUP function in B4:C6 to “look up” the paired scenario corresponding to the integer in A4:A6.

Decision Models -- Prof. Juran99

Decision Models -- Prof. Juran100

Decision Models -- Prof. Juran101 Summary Finance Simulation Models Forecasting –Retirement Planning –Butterfly Strategy Risk Management –Introduction to VaR –Currency Risk Using Historical Data in Simulations –Parametric Approach –Resampling Approach