Monte Carlo Simulation Natalia A. Humphreys April 6, 2012 University of Texas at Dallas

Monte Carlo Simulation

Natalia A. HumphreysApril 6, 2012

University of Texas at Dallas

Aknowledgement Wayne L. Winston, “Microsoft Excel Data

Analysis and Business Modeling”, 2004

Overview Part I

Questions answered with the help of MCS

History

Typical simulations

Part II: Simulation examples

Part III: Advantages of MCS over deterministic analysis

Challenges

We are constantly faced with uncertainty, ambiguity, and variability.

Risk analysis is part of every decision we make.

We’d like to accurately predict (estimate) the probabilities of uncertain events.

Monte Carlo simulation enables us to model situations that present uncertainty and play them out thousands of times on a computer.

Questions answered with the help of MCS

How should a greeting card company determine how many cards to produce?

How should a car dealership determine how many cars to order?

What is the probability that a new product’s cash flows will have a positive net present value (NPV)?

What is the riskiness of an investment portfolio?

Modeling with MCS Monte Carlo Simulation (MCS) lets you see all

the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.

MCS: Where did the Name Come From?

During the 1930s and 1940s, many computer simulations were performed to estimate the probability that the chain reaction needed for the atom bomb would work successfully.

The Monte Carlo method was coined then by the physicists John von Neumann, Stanislaw Ulam and Nicholas Metropolis, while they were working on this and other nuclear weapon projects (Manhattan Project) in the Los Alamos National Laboratory.

It was named in homage to the Monte Carlo Casino, a famous casino in the Monaco resort Monte Carlo where Ulam's uncle would often gamble away his money.

Who Uses MCS? General Motors (GM)

Procter and Gamble (P&G)

Eli Lilly

Wall Street firms

Sears

Financial planners

Other companies, organizations and individuals

MCS Use General Motors (GM), Procter and Gamble

(P&G), and Eli Lilly use simulation to estimate both the average return and the riskiness of new products.

MCS Use: GM Forecast net income for the corporation

Predict structural costs and purchasing costs

Determine its susceptibility to different risks: Interest rate changes

Exchange rate fluctuations

MCS Use: Lilly

Determine the optimal plant capacity that should be built for each drug

MCS Use: Wall Street

Price complex financial derivatives

Determine the Value at Risk (VaR) of investment portfolios. By definition, Value at Risk at security level p

for a random variable X is the number VaR_p(X) such that

Pr(X<VaR_p(X))=p

In practice, p is selected to be close to 1: 95%, 99%, 99.5%

MCS Use: Procter & Gamble

Model and optimally hedge foreign exchange risk

MCS Use: Sears How many units of each product line should

be ordered from suppliers

MCS Use: Financial Planners

Determine optimal investment strategies for their clients’ retirement.

MCS Use: Others Value “real options”:

Value of an option to expand, contract, or postpone a project

MCS Applications Physical Sciences

Engineering

Computational Biology

Applied Statistics

Games

Design and visuals

Finance and business (Actuarial Science)

Telecommunications

Mathematics

Part II We’ll now discuss how Monte Carlo simulation

works by looking at a few simulation examples

=RAND() function When you enter the formula =RAND() in a

cell, you get a number that is equally likely to assume any value between 0 and 1.

Get a number less than or equal to 0.25 around 25% of the time

Get a number that is at least 0.9 around 10% of the time

Example 1: Discrete Random Variable

Simulation Demand for a calendar is governed by the

following discrete r.v.:

DEMAND PROBABILITY

10,000 0.10

20,000 0.35

40,000 0.30

60,000 .25

Discrete r.v. Simulation(cont.)

How can we have Excel play out, or simulate, this demand for calendars many times?

We associate each possible value of the RAND function with a possible demand for calendars.

Discr r.v. Sim (cont.)

The following assignment ensures that a demand of 10,000 will occur 10 percent of the time, and so on.

DEMAND RANDOM NUMBER ASSIGNED

10,000 Less than 0.10

20,000 Greater than or equal to 0.10 and less than 0.45

40,000 Greater than or equal to 0.45 and less than 0.75

60,000 Greater than or equal to 0.75

Discr r.v. Sim (cont.) Creating the following cutoff table, we then

use it to look up the values “assigned” to each random number:

CUTOFF DEMAND

0 10,000

0.1 20,000

0.45 40,000

0.75 60,000

TRIAL RAND SIM DEMAND

1 0.823097422

60,000

2 0.076074298

10,000

3 0.364201634

20,000

4 0.698116365

40,000


The function used to create the values in the third column of the second table is called the VLOOKUP function.

Its syntax in Excel is:

VLOOKUP( lookup_value, table_array, col_index_num, range_lookup )


Thus, the VLOOKUP(0.823097422, LOOKUP, 2, 1)=60,000

TRUE=1, FALSE=0

If VLOOKUP can't find lookup value, and range lookup is TRUE, it uses the largest value that is less than or equal to lookup value.

Discr r.v. Sim (cont.) If we simulate 400 values of calendar

demand and then calculate the fraction of time each demand appears in the simulation, we’ll get a table similar to the following:

DEMAND FRACTION OF TIME

10,000 0.1025020,000 0.3550040,000 0.2925060,000 0.25000

DEMAND PROBABILITY

10,000 0.10

20,000 0.35

40,000 0.30

60,000 0.25

Example 2: Normal Random Variable

Simulation Suppose we want to simulate 400 trials or

iterations for a normal r.v. with a mean μ=40,000 and standard deviation σ=10,000

What is a normal random variable? Let us first define the standard normal random

variable.

Standard Normal Random Variable

Its distribution has a form of a “bell” curve around the zero.

Standard Normal Distribution Table is a table that shows probability that a standard normal random variable Z is less than a number z:

Φ(z)=Pr(Z<z)

A standard normal r.v. Z is a r.v. with μ=0 and σ=1

Connection between any Normal r.v. and a Standard Normal r.v.

If Z is N(0, 1) and is Y is N(μ, σ^2), then

Y=σZ+μ

Normal Random Variable Simulation

Suppose we want to simulate 400 trials or iterations for a normal r.v. with a mean μ=40,000 and standard deviation σ=10,000

The formula NORMINV(RAND(), μ, σ) will generate a simulated value of a normal r.v. having a mean μand standard deviation σ.

Normal r.v. Sim (cont.)

33,518.16 = NORMINV(0.258433031, 40,000, 10,000)

This value could also be looked up using the Standard Normal Distribution table.

TRIAL RAND NORMAL RV

1 0.258433031 33,518.16

2 0.344835199 36,006.98

3 0.927522163 54,575.82

4 0.248403053 33,204.76

Example 3: How Many Cards to Produce?

Suppose the demand for a Valentine’s Day card is governed by the following discrete r.v.:

DEMAND PROBABILITY

10,000 0.10

20,000 0.35

40,000 0.30

60,000 .25

Cards to Produce? (cont.)

The greeting card sells for $4.00

The variable cost of producing each card is $1.50

Leftover cards will be disposed at $0.20 per card

How many cards should be printed to get the highest profit?


We simulate each possible production quantity (10,000, 20,000, 40,000 or 60000) many times (e.g. 1,000 iterations)

Then we determine which order quantity yields the maximum average profit over the 1,000 iterations


1 produced 10,000

2 rand0.40092709

13 demandcard 20,000

4 unit prod cost $1.50 5 unit price $4.00

6 unit disp cost $0.20 7 revenue $40,000.00 8 total var cost $15,000.00

9 total disposing cost $- 10 profit $25,000.00


Our sales and cost parameters are in 4, 5, and 6

Enter a trial production quantity in 1

Create a random number in 2 with =RAND()

Simulate demand for the card in 3 with VLOOKUP(rand, lookup, 2)

The number of unites sold is

MIN (Production Quantity, Demand)


Revenue in 7: MIN (Produced, Demand)*unit price

Total production cost in 8: produced*unit production cost

If we produce more cards than are demanded, the number of units left over equals production minus demand


Disposal cost in 9:

unit disposal cost*MAX(produced-demand, 0)

Total profit in 10:

Revenue – total var cost – total disposing cost


We would like an efficient way to calculate profit for each production quantity

We’ll use a two-way data table

mean (ave profit) 24,985 45,984 57,311 44,218

st dev (risk) -

12,321.19

48,346.89

73,622.44

25,000 10,000 20,000 40,000 60,000

1 25000 50000 16000 -60000

2 25000 50000 100000 66000

3 25000 50000 16000 66000

4 25000 50000 100000 150000

5 25000 50000 100000 -18000


Enter 1-1000 on the left corresponding to our 1,000 trials

Enter possible production quantities (third row)

We want to calculate profit for each trial number and each production quantity

Refer to the formula for profit in the upper left cell of our data table by entering =B11

We are now ready to trick Excel into simulating 1,000 iterations of demand for each production quantity.


Select the table range and then click Table on the Data menu.

Click on any blank cell (e.g. I14) as the column input cell and choose production quantity (cell B1) as the row input cell.

We calculate the average simulated profit for each production quantity

We calculate the standard deviation of simulated profits for each production quantity

Cards to Produce? Conclusion

Producing 40,000 cards always yields the largest expected profit

However, it also appear to have a large standard deviation (risk)

The Impact of Risk in Our Decision

Producing 20,000 cards instead of 40,000, the expected profits drop by about 22%, but the risk drops almost 73%.

Therefore, if we are extremely risk averse, producing 20,000 cards might be the right decision.

Note that producing 10,000 cards always has a std.dev. of zero cards because if we produce 10,000 cards we will always sell all of them and have none left over.

Confidence Interval for Mean Profit

Into what interval are we 95% sure the true mean will fall?

This interval is called the 95% confidence interval for mean profit.

It’s computed by the following formula:

Mean Profit ±(1.96*profit std.dev.)/√(number iterations)

In our example: (53,650.46 59,628.26 )

Problems

1 A GMC dealer believes that demand for 2005 Envoys will normally be distributed with a mean of 200 and standard deviation of 30. His cost of receiving an Envoy is $25,000, and he sells an Envoy for $40,000. Half of all leftover Envoys can be sold for $30,000. His is considering ordering 200, 220, 240, 260, 280, and 300 Envoys. How many should he order?

Problems (cont.)2 A small supermarket is trying to determine

how many copies of Newsweek magazine they should order each week. They believe their demand for Newsweek is governed by the following discrete random variable

DEMAND PROBABILITY

15 0.10

20 0.20

25 0.30

30 0.25

35 0.15

Problems (cont.)

2 The supermarket pays $1.00 for each copy of Newsweek and sells each copy for $1.95. They can return each unsold copy of Newsweek for $0.50. How many copies of Newsweek should the store order to maximize its profit?

Part III: Advantages of MCS

In conclusion, we’ll discuss some advantages of MCS over deterministic, or “single-point estimate” analysis.

Advantages of MCSMCS provides a number of advantages over

deterministic, or “single-point estimate” analysis:

Probabilistic Results

Graphical Results

Sensitivity Analysis

Scenario Analysis

Correlation of Inputs

Probabilistic Results Results show not only what could happen, but

how likely each outcome is.

Graphical Results Because of the data a Monte Carlo simulation

generates, it’s easy to create graphs of different outcomes and their chances of occurrence.

This is important for communicating findings to other stakeholders.

Sensitivity Analysis With just a few cases, deterministic analysis

makes it difficult to see which variables impact the outcome the most.

In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.

Scenario Analysis In deterministic models, it’s very difficult to

model different combinations of values for different inputs to see the effects of truly different scenarios.

Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred.

This is invaluable for pursuing further analysis.

Correlation of Inputs In Monte Carlo simulation, it’s possible to

model interdependent relationships between input variables.

It’s important for accuracy to represent how, in reality, when some factors go up, others go up or down accordingly.

References

Wayne L. Winston, “Microsoft Excel Data Analysis and Business Modeling”, 2004 http://office.microsoft.com/en-us/excel-help/introduction-to-monte-carlo-simulation-HA001111893.aspx

Monte Carlo Simulation http://www.palisade.com/risk/monte_carlo_simulation.asp

Documents

Monte Carlo Simulation Natalia A. Humphreys April 6, 2012 University of Texas at Dallas