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Financial Economic

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Homework 2 MF640

1. What is the meaning of Lagrange Multiplier?

Ans The Lagrange multipliers method is one of methods for solving constrained extrema problems. Assume that we have a function f of n variables {y = f (x1,..., x n)} and we would like to find the value of x1,..., xn that will maximize or minimize this function subject to constraint g (x1,..., xn). The Lagrange multipliers method is based on setting up the new function (the Lagrange function)

                        L(x1,..., x n,λ) = f(x1,..., x n)  +λg (x1,..., x n) 

where λ is an additional variable called the Lagrange multiplier. From the Lagrange equation, the conditions for a critical point are
                                        
L´ x1 = f´x1 +λg´ x1
                                        
L´ x2 = f´x2 +λg´ x2
                                                …
                                        
L´ xn = f´xn +λg´ xn

                                        L´ λ = g (x1,..., xn)

Rearrange the first n equations and set it equal to zero as

                                        

                                λ or    = λ[pic 3][pic 4]

The Lagrange multipliers can interpret that the value of optimal f will change λ unit if the exogenous variable (g(x)) change 1 unit. This show the marginal effect of changing in exogenous variable affect to the value of optimal objective variable (f). If the Lagrange multipliers are too high, the value of f will change more than the constraint which the Lagrange multiplier is low.

3. What is the optimum point of investment? If the utility function is quadratic utility function and

Return

Standard Deviation

[pic 5]

Investment 1

0.15

0.2

-0.2

Investment 2

0.08

0.1


Begin with the efficient Frontier (Calculate from excel), we separate weight of investment into 11 groups.

Portfolio

Weight 1

Weight 2

Expected Return(%)

S.D.(port%)

1

0

1

8

1

2

0.1

0.9

8.7

0.778

3

0.2

0.8

9.4

0.672

4

0.3

0.7

10.1

0.682

5

0.4

0.6

10.8

0.808

6

0.5

0.5

11.5

1.05

7

0.6

0.4

12.2

1.408

8

0.7

0.3

12.9

1.882

9

0.8

0.2

13.6

2.472

10

0.9

0.1

14.3

3.178

11

1

0

15

4

[pic 6]

After that we create the quadratic utility function as
and assume that a = 0 (it doesn’t play any role). .
However, we have to create the conditions of b and c to support the theory.[pic 7][pic 8]

Condition

Reason

  1. b>0 and c<0

Utility has positive relationship with return but negative relationship with S.D. (risk).

  1. b+ 2cRp > 0

Positive marginal Utility ( first diff > 0)

  1. 2c < 0

Relative Risk aversion (second diff < 0 )


We find that the optimum point of investment depend on preference of investor (b and c). If you change value of b and c, the optimum point will change as table below

  1. b = 0.3 and c = -0.7

Portfolio

Expected Return

S.D.

Expected Utility

b+2c*Rp

Condition

Condition

1

0.08

0.01

0.01945

0.188

Pass

a

0

Assume

2

0.087

0.00778

0.02075933

0.1782

Pass

b > 0

0.3

Pass

3

0.094

0.00672

0.021983189

0.1684

Pass

c < 0

-0.7

Pass

4

0.101

0.00682

0.023126741

0.1586

Pass

b+2c*Rp > 0

Table

 

5

0.108

0.00808

0.0241895

0.1488

Pass

2c < 0

-1.4

Pass

6

0.115

0.0105

0.025165325

0.139

Pass

7

0.122

0.01408

0.026042428

0.1292

Pass

8

0.129

0.01882

0.026803365

0.1194

Pass

9

0.136

0.02472

0.027425045

0.1096

Pass

10

0.143

0.03178

0.027878722

0.0998

Pass

11

0.15

0.04

0.02813

0.09

Pass

  1. b = 0.3 and c = -0.6

Portfolio

Expected Return

S.D.

Expected Utility

b+2c*Rp

Condition

1

0.08

0.01

0.0201

0.204

Pass

2

0.087

0.00778

0.021522283

0.1956

Pass

3

0.094

0.00672

0.022871305

0.1872

Pass

4

0.101

0.00682

0.024151493

0.1788

Pass

5

0.108

0.00808

0.025362428

0.1704

Pass

6

0.115

0.0105

0.02649885

0.162

Pass

7

0.122

0.01408

0.027550652

0.1536

Pass

8

0.129

0.01882

0.028502885

0.1452

Pass

9

0.136

0.02472

0.029335753

0.1368

Pass

10

0.143

0.03178

0.030024619

0.1284

Pass

11

0.15

0.04

0.03054

0.12

Pass

...

...

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