Project Screening by Dominance

(1)

Economic Risk and Decision Analysis Economic Risk and Decision Analysis

for Oil and Gas Industry for Oil and Gas Industry

CE81.9008 CE81.9008

School of Engineering and Technology School of Engineering and Technology

Asian Institute of Technology Asian Institute of Technology

January Semester January Semester

Presented by Presented by Dr. Thitisak Boonpramote Dr. Thitisak Boonpramote

Department of Mining and Petroleum Engineering,

Department of Mining and Petroleum Engineering, ChulalongkornChulalongkornUniversityUniversity

Project Screening by Project Screening by

Dominance

(2)

Investments Criteria Investments Criteria

Maximum return

If the return is risk-free (certain),all investors prefer the higher return Maximum expected return

If two risky assets have the same varianceof the returns, risk-averse investors prefer the one with the higher expected return*

Minimum variance of the return

If two risky assets have the same expected return, risk-averse investors prefer the one with the lower variance of return*

Risk aversion

Investors prefer a certain dollarto a lottery with an expected returnof one dollar

Mean-Variance Criterion

Chose the investment with the lower variance of returnand higher expected return

Mean Mean- -Variance Criterion Variance Criterion

Decision makermay use EMVand standard deviation to screen or rank alternatives

Mean-variance approachfavored by some risk-averse decision makers seeks to choose alternative that yields highest expected returnwith lowest variance

(3)

Mean Mean- -Standard Deviation Screening Method Standard Deviation Screening Method

Approach more appropriate when probability

distributionof each alternative can be represented by mean and standard deviation

Normal distribution good example of appropriate distribution

When distribution notdescribed completely by mean and standard deviation, best to compare distributions themselves

Dominance Dominance

When we can compare pdfand cdfof alternatives, we can use dominance rulesto choose between

alternatives

Situations include

Deterministic dominance–pdf’s, cdf’s, don’t intersect, one alternative always better

(4)

Deterministic Dominance Deterministic Dominance

The PDFof alternative B is always to the rightof the PDF of alternative A.

PDF for the two investment alternatives A and B do not intersect.

Therefore, alternative A is clearly dominatedby alternative B

The EMV of B being certainly higherthan A

This situation is referred to as deterministic dominance.

Alternative B has a higher expected meanand lower

(5)

First

First- -Degree Stochastic Dominance (FSD) Degree Stochastic Dominance (FSD)

First

First- -Degree Stochastic Dominance (FSD) Degree Stochastic Dominance (FSD)

PDFof two investments do intersect

But the corresponding CDF do not intersect, then a condition of first degree stochastic dominanceexists.

It cannot be said with certaintyone alternative will produce higher EMV than the other

(6)

More Complex Situation More Complex Situation

Second

Second- -Degree Stochastic Dominance (SSD) Degree Stochastic Dominance (SSD)

Both the PDFsand their corresponding CDFsof alternatives intersect.

If the alternatives have the same expected value, but the S.D. of alternative F is more than the S.D. of alternative E. Therefore, alternative F is more risky.

The CDF shows up to the point of crossover, investment E dominated investment F while after the crossover point investment F dominates investment E.

This situation is called second-degree stochastic dominance

Comparison of the areas between the F dominated partand the E dominated partwill give the overall extent of dominance. The part with

(7)

Alternativea_jdominates a_iin the sense of SSD if

for all zover y.

0 )]

( ) (

[ − ≥

∫

∞

− z

j

i y F y dy

F

Second

Second- -Degree Stochastic Dominance (SSD) Degree Stochastic Dominance (SSD)

Mean Mean- -Standard Deviation Screening Method Standard Deviation Screening Method

(8)

Mean Mean- -Standard Deviation Screening Method Standard Deviation Screening Method

Investments 1, 7, 8, 9, and 10 provide greater EMV for given level of risk than other investment opportunities

Determines “efficient frontier”

Choice between investments depends on how risk aversedecision maker is

Investment 10 offers greatest reward, but is highest risk

Application of Dominance Rules Application of Dominance Rules

5.557 111.141

1.320 26.401

6.293 125.863

0.05 65

11.057 73.712

3.104 20.693

13.112 87.411

0.15 50

MSTB

8.536 34.142

3.662 14.646

11.362 45.448

0.25 35

MSTB

0.225 0.750

2.620 8.733

1.307 4.357

0.30 20

MSTB

0 0

-7.500 -30.000

0.25 Dry hole

EMV NPV

Back in with 37.5%

Farm out with ORI Drill with 37.5%

Proba- bility Out-

come State

(9)

Application of Dominance Rules Application of Dominance Rules

Assume normal distributions for each case

Allows use of EMV, standard deviation to generate cdf

Application of Dominance Rules

(10)

Application of Dominance Rules Application of Dominance Rules

Back-in optiondominates drill option for 0<EMV<$27M

Drill optiondominates back-in option for EMV>$27M

Area dominated by back-in optionslightly larger than area dominated by drill option

Back-in option has second-degree stochastic dominanceover drill option