tm 661 engineering economics for managers
DESCRIPTION
TM 661 Engineering Economics for Managers. Risk Analysis. A A A A A. MARR = 15%. 1 2 3 4 5. 10,000. Motivation. Suppose we have the following cash flow diagram. NPW = -10,000 + A(P/A, 15, 5). 2. ,. 000. p. . 1. /. 6. . - PowerPoint PPT PresentationTRANSCRIPT
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ENGM 661Engineering Economics
for Managers
Risk AnalysisRisk Analysis
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Motivation
Suppose we have the following cash flow diagram.
NPW = -10,000 + A(P/A, 15, 5)
1 2 3 4 5
A A A A A
10,000
MARR = 15%
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Motivation
Now suppose that the annual return A is a random variable governed by the discrete distribution:
A
p
p
p
2 000 1 6
3 000 2 3
4 000 1 6
, /
, /
, /
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Motivation
A
p
p
p
2 000 1 6
3 000 2 3
4 000 1 6
, /
, /
, /
For A = 2,000, we have
NPW = -10,000 + 2,000(P/A, 15, 5)
= -3,296
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Motivation
A
p
p
p
2 000 1 6
3 000 2 3
4 000 1 6
, /
, /
, /
For A = 3,000, we have
NPW = -10,000 + 3,000(P/A, 15, 5)
= 56
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Motivation
A
p
p
p
2 000 1 6
3 000 2 3
4 000 1 6
, /
, /
, /
For A = 4,000, we have
NPW = -10,000 + 4,000(P/A, 15, 5)
= 3,409
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Motivation
There is a one-for-one mapping for each value of A, a random variable, to each value of NPW, also a random variable.
A 2,000 3,000 4,000
p(A) 1/6 2/3 1/6
NPW -3,296 56 3,409
p(NPW) 1/6 2/3 1/6
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Spreadsheet
MARR = 15%P(A ) = 1/6 2/3 1/6
A 2,000 3,000 4,000t CF CF CF0 (10,000) (10,000) (10,000)1 2,000 3,000 4,0002 2,000 3,000 4,0003 2,000 3,000 4,0004 2,000 3,000 4,0005 2,000 3,000 4,000
NPV = (3,296) 56 3,409P(NPV) = 1/6 2/3 1/6
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
Now Suppose the return in each year is a randomvariable governed by the some probability distribution.
NPW = -10,000 + A1(1+i)-1 + A2(1+i)-2 + . . . + A5(1+i)-5
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Risk Analysis
10 000
1
5
, a Att
t
1( )where a it
t
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
NPW = -10,000 + A1(1+i)-1 + A2(1+i)-2 + . . . + A5(1+i)-5
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Risk Analysis
Now suppose At iid N(3,000, 250)
NPW is a linear combination of normals
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Risk Analysis
Now suppose At iid N(3,000, 250)
NPW is a linear combination of normals
NPW Normal
Introduction toProbability & Statistics
The Central Limit TheoremThe Central Limit Theorem
Introduction toProbability & Statistics
The Central Limit TheoremThe Central Limit Theorem
Central Limit
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Risk Analysis
Now suppose At iid N(3,000, 250)
NPW is a linear combination of normals
NPW Normal
NPW N(NPW, NPW)
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Mean
NPW tt
tE NPW E a A [ ] [ ,10 000
1
5
Recall: E[Z] = E[X1] + E[X2]
E[aX+b] = aE[X] + b
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Mean
NPW tt
tE NPW E a A [ ] [ ,10 000
1
5
Recall: E[Z] = E[X1] + E[X2]
E[aX+b] = aE[X] + b
10 000
1
5
, [ ]a E Att
t NPW
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Mean
10 000
1
5
, [ ]a E Att
t NPW
but, E[At] = 3,000
10 000
1
5
, [ ]a 3,000tt
NPW
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Mean
10 000
1
5
, [ ]a 3,000tt
NPW
= -10,000 + 3,000 a tt
1
5
= -10,000 + 3,000 (1+i)-t
t
1
5
= -10,000 + 3,000(P/A, i, 5)
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Variance
2 2 10 000NPW t ta A ( , )
Recall: 2(z) = 2(x) + 2(y)
2(ax+b) = a22
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Variance
2 2 10 000NPW t ta A ( , )
Recall: 2(z) = 2(x) + 2(y)
2(ax+b) = a22
2 2 2NPW t Aa t
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Variance
but, 2 2250( )At
2 2 2
NPW ta (At)
2 2 2250NPW ta ( )
= (250)2 [(1+i)-2 + (1+i)-4 + . . . + (1+i)-10]
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Variance
2 2 2250NPW ta ( )
= (250)2 [(1+i)-2 + (1+i)-4 + . . . + (1+i)-10]
Note that [(1+i)-2 + (1+i)-4 + . . . + (1+i)-10 ] is just a 5 period annuity factor where the period is 2 years.
A=(250)2
1 2 3 4 5 6 7 8 9 10
P=2NPW
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Variance
2 2 2250NPW ta ( )
= (250)2 [(1+i)-2 + (1+i)-4 + . . . + (1+i)-10]
A=(250)2
1 2 3 4 5 6 7 8 9 10
P=2NPW
= (250)2(P/A, ieff, 5) , ieff = (1+i)2 -1
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
NPW = -10,000 + 3,000(P/A, 15, 5)
= -10,000 + 3,000(3.3522)
= $56
At iid N(3,000, 250)
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
NPW = -10,000 + 3,000(P/A, 15, 5)
= -10,000 + 3,000(3.3522)
= $56
At iid N(3,000, 250)
ieff = (1.15)2 - 1 = 32.25%
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
NPW = -10,000 + 3,000(P/A, 15, 5)
= -10,000 + 3,000(3.3522)
= $56
2NPW = (250)2(P/A, 32.25, 5)
= 62,500(2.3343) = 145,894
At iid N(3,000, 250)
ieff = (1.15)2 - 1 = 32.25%
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
NPW = $56
2NPW = 145,894 = 382
At iid N(3,000, 250)
NPW 382)
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
MARR = 15%
At iid N(3,000, 250)
NPW 382)
N(56, 382)
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
NPW 382)
N(56, 382)
P NPW PNPW NPW
NPW
( )
00 56
382
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Risk Analysis
1 2 3 4 5
A1 A2 A3 A4 A5
10,000
NPW 382)
N(56, 382)
P NPW PNPW NPW
NPW
( )
00 56
382
= P( Z < -0.15 ) = 0.44
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Review
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You are given the following cash flow diagram:
Class Problem
5, 000 , 0.6
7, 000 , 0.4
If Ai iid
A1 A2
10,000
Compute the distribution for the Net Present Worth if the MARR = 15%.
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Class Problem
MARR = 0.15
t R1 R2 R3 R4
0 (10,000) (10,000) (10,000) (10,000)
1 5,000 5,000 7,000 7,000
2 5,000 7,000 5,000 7,000
NPW (1,871) (359) (132) 1,380
P(NPW) 0.36 0.24 0.24 0.16
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You are given the following cash flow diagram:
Class Problem
If the MARR = 15%, what is the probability this investment alternative is no good?
A1 A2 A3 A4 A5
35,000 If Ai iid N(10,000, 300)
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Class Problem
E[NPW] = -35,000 + 10,000(P/A, 15, 5)
= -35,000 + 10,000(3.3522)= - 1,478
2(NPW)3002 [(1.15) 2 (1.15) 4 . ..(1.15) 10 ]
= 3002 (2.3343)
= 210,087
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Class Problem
NPW N(-1,478 , 458)
P{NPW < 0} =
PNPW
0 ( 1,478)
458
= P{Z < 3.27}1.0
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A Critical ThunkA1 A2 A3 A4 A5
35,000
If Ai iid N(10,000, 300)
Max Ai 10,900
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A Critical ThunkA1 A2 A3 A4 A5
35,000
If Max Ai 10,900
NPW = -35,000 + 10,900(P/A, 15, 5)
= -35,000 + 10,900(3.3522)
= 1,539
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If Ai iid U(5000, 7000)
A Twist
Suppose we have the following cash flow diagram.
A1 A2
10,000
Now how can we compute the distributionof the NPW? MARR = 15%.
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Solution Alternatives
Assume normality
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Solution Alternatives
Assume normality Upper/Lower Bounds Laplace Transforms Transformation/Convolution Simulation
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Simulation
A1 A2
10,000
Let us arbitrarily pick a value for A1 and A2 in the uniform range (5000, 7000). Say A1 = 5,740 and A2 = 6,500.
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Simulation
A1 A2
10,000
5,740 6,500
10,000
NPW = -10,000 + 5,740(1.15)-1 + 6,500(1.15)-2
= (93.96)
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Simulation
A1 A2
10,000
5,740 6,500
10,000
We now have one realization of NPW for a given realization of A1 and A2.
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Simulation
A1 A2
10,000
5,740 6,500
10,000
We now have one realization of NPW for a given realization of A1 and A2. Choose 2 new values forA1, A2.
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A1 = 6,820 A2 = 6,218
A1 A2
10,000
6,820 6,218
10,000
NPW = -10,000 + 6,820(1.15)-1 + 6,218(1.15)-2
= 632.14
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Summary
A1 A2 NPW
5,740 6,500 (93.96)6,820 6,218 632.14
Choose 2 new values.
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A1 = 5,273 A2 = 6,422
A1 A2
10,000
5,273 6,422
10,000
NPW = -10,000 + 5,273(1.15)-1 + 6,422(1.15)-2
= (558.83)
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Summary
A1 A2 NPW
5,740 6,500 (93.96)6,820 6,218 632.145,273 6,422 (558.83)
Choose 2 new values.
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Summary
A1 A2 NPW
5,740 6,500 (93.96)6,820 6,218 632.145,273 6,422 (558.83) . . .6,855 5,947 457.66
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Simulation
With enough realizations of A1, A2, and NPW, we can begin to get a sense of the distribution of the NPW.
-1,871 0 1,380 NPW
Freq.
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Simulation
What we now need is a formal method of selecting random values for A1 and A2 to avoid selection bias.
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Simulation
What we now need is a formal method of selecting random values for A1 and A2 to avoid selection bias.
Recall the uniform
5,000 7,000
1/2,000
f(x)
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Simulation
The uniform has cumulative distribution given by:
5,000 7,000
1
F(x)
F( x)
0 , x5,000
1 , x7,000
x 5,0002,000
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Simulation
Recall that 0 < F(x) < 1. Using modulo arithmetic, it is fairly easy to generate random numbers between 0 and 1 (Rand Function in Excel). Let P be a random variable uniformly from 0 to 1.
P U(0,1)
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Simulation
Recall that 0 < F(x) < 1. Using modulo arithmetic, it is fairly easy to generate random numbers between 0 and 1. Let P be a random variable uniformly from 0 to 1.
P U(0,1)Algorithm: 1. Randomly generate P 2. Let P = F(x) 3. Solve for x = F-1(p)
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Simulation
1. Randomly generate P U(0,1). P = .7
5,000 7,000
1
F(x)
F( x)x 5,000
2,000
5,000 < x < 7,000
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Simulation
1. Randomly generate P U(0,1). P = .72. Let P = F(x).
5,000 7,000
1
F(x)
F( x)x 5,000
2,000
5,000 < x < 7,000
.7
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Simulation
1. Randomly generate P U(0,1). P = .72. Let P = F(x).3. x = F-1(p).
5,000 7,000
1
F(x)
F( x)x 5,000
2,000
5,000 < x < 7,000
.7
6,400
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Formal Derivation
Recall, for
5,000 7,000
1
F(x)
F( x)x 5,000
2,0005,000 < x < 7,000. Then
P
Px
5 000
7 000 5 000
,
, ,
x 5 000
2 000
,
,
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Formal Derivation
Solving for x = F-1(p),
5,000 7,000
1
F(x)
P
x
x P 5 000 2 000, ,
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Formal Derivation
Solving for x = F-1(p),
5,000 7,000
1
F(x)
P
x
x P 5 000 2 000, ,
Note: 1. P = 0 x = 5,000
2. P = 1 x = 7,000
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Risk Analysis (Excel)
A t = a + (b-a) P NPW = 282
t P = U(0,1) At n NPWn
0 (20,000) 1 (672)
1 0.6979 6,396 2 726
2 0.1273 5,255 3 (903)
3 0.5949 6,190 4 (789)
4 0.8161 6,632 5 (1,006)
5 0.4013 5,803 6 904
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Class Problem
You are given the following cash flow diagram. The Ai are iid shifted exponentials with location parameter a = 1,000 and scale parameter = 3,000. The cumulative is then given by
7,000
A1 A2 A3
F x e x( ) ( , )/ , 1 1 000 3 000 , x > 1,000
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Class Problem
You are given the first 3 random numbers U(0,1) as follows:
P1 = 0.8
P2 = 0.3
P3 = 0.5
You are to compute one realization for the NPW.MARR = 15%.
7,000
A1 A2 A3
F x e x( ) ( , )/ , 1 1 000 3 000
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Class Problem
P1 e
x 1000
3000
e
x 1000
3000
1 P
x 1000
3000
ln(1 P)
x1,000 3,000 ln(1 P)
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Class Problemx1,000 3,000ln(1 P)
A1 = 1,000 - 3000 ln(1 - .8)
= 5,828
A2 = 1,000 - 3000 ln(1 - .3)
= 2,070
A3 = 1,000 - 3000 ln(1 - .5)
= 3,079
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Class Problem
7,000
5,8282,0703,079
NPW = -7,000 + 5,828(1.15)-1 + 2,070(1.15)-2 + 3,079(1.15)-3
= 1,657
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Class Problem
You are given the following cash flow diagram. The Ai are iid gammas with shape parameter = 4 and scale parameter = 3,000. The density function is given by 7,000
A1 A2 A3
f x x e x( )( )
/
1 , x > 0
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Class Problem
You are given the first 3 random numbers U(0,1) as follows:
P1 = 0.8
P2 = 0.3
P3 = 0.5
You are to compute one realization for the NPW.MARR = 15%.
7,000
A1 A2 A3
![Page 70: TM 661 Engineering Economics for Managers](https://reader030.vdocuments.site/reader030/viewer/2022032709/56812f8a550346895d950709/html5/thumbnails/70.jpg)
Class ProblemFor = integer, the cumulative distribution function is given by
Set P = F(x), solve for x
0,!
)/(1)(
1
0
/
Xj
xexF
j
jx
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Class Problem
For general (not integer),
F(x) = not analytic
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Class Problem
For general (not integer),
F(x) = not analytic
No Inverse
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@RISK