Introduction Risk Management

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This presentation belongs to the course "Finance for Exchange" at HAN University of Applied Science, Arnhem (The Netherlands).

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Introduction Risk Managment

Finance for Exchange

Witek ten Hove

Introduction Risk Management

Risk and quality Risk analysis Risk reporting Risk and behavior Risk instruments

Risk and quality

Decrease uncertainty for stakeholders: Suppliers of capital (cash flows) Customers (product) Suppliers (sales and payments) Employees (career and reward)

Alea Iacta Est

Probability of loss within one month

Price- {combinations}

2 - {(1,1)} 3 - {(1,2), (2,1)} 4 - {(1,3), (2,2), (3,1)} 5 - {(1,4), (2,3), (3,2), (4,1)} 6 - {(1,5), (2,4), (3,3), (4,2), (5,1)} 7 - {(1,6), (2,5), (3,4), (4,3), (5,2),

(6,1)} 8 - {(2,6), (3,5), (4,4), (5,3), (6,2)} 9 - {(3,6), (4,5), (5,4), (6,3)} 10 - {(4,6), (5,5), (6,4)} 11 - {(5,6), (6,5)} 12 - {(6,6)}

Total: 36 combinations

Source: http://www.futureaccountant.com/theory-of-expectation-random-variable/problems-solutions/throwing-rolling-dice.php

Probability of default within two months

Result- {combinations}

-/- 200 {(1,1)}

-/- 100 {(1,2), (2,1)}

0 {(1,3), (2,2), (3,1)}

100 {(1,4), (2,3), (3,2), (4,1)}

200 {(1,5), (2,4), (3,3), (4,2), (5,1)}

300 {(1,6), (2,5), (3,4), (4,3), (5,2), (6,1)}

400 {(2,6), (3,5), (4,4), (5,3), (6,2)}

500 {(3,6), (4,5), (5,4), (6,3)}

600 {(4,6), (5,5), (6,4)}

700 {(5,6), (6,5)}

800 {(6,6)}

Totaal: 36 combinations

Month 1   Month 2 Total

ResultProbability and Result Probability Result Probability

-200 1/36   default 36/36 -200 1/36

or            

-100 1/18   -200 1/36 -300 1/648

  -100 1/18 -200 1/324

or            

0 1/12   -200 1/36 -200 1/432

Total   3,5%

Probability of default within two months with interest jump

Result- {combinations}

-/- 600 {(1,1)}

-/- 500 {(1,2), (2,1)}

-/- 400 {(1,3), (2,2), (3,1)}

-/- 300 {(1,4), (2,3), (3,2), (4,1)}

-/- 200 {(1,5), (2,4), (3,3), (4,2), (5,1)}

-/- 100 {(1,6), (2,5), (3,4), (4,3), (5,2), (6,1)}

0 {(2,6), (3,5), (4,4), (5,3), (6,2)}

100 {(3,6), (4,5), (5,4), (6,3)}

200 {(4,6), (5,5), (6,4)}

300 {(5,6), (6,5)}

400 {(6,6)}

Totaal: 36 combinations

Month 1   Month 2 TotalResult Probability and Result Probability Result Probability

-600 1/36   1 -600 1/36 or            

-500 1/18   1 -500 1/18 or            

-400 1/12   1 -400 1/12 or            

-300 1/9   1 -300 1/12 or            

-200 5/36   1 -200 1/12 Totaal 41,7%      

or            -100 1/6   -600 1/36 -700 1/216

  -500 1/18 -600 1/108   -400 1/12 -500 1/72   -300 1/9 -400 1/54   -200 5/36 -300 5/216   -100 1/6 -200 1/36 or            

0 5/36   -600 1/36 -600 5/1296   -500 1/18 -500 5/648   -400 1/12 -400 5/432   -300 1/9 -300 5/324   -200 5/36 -200 25/1296or            

100 5/36   -600 1/36 -500 5/1296   -500 1/18 -400 5/648   -400 1/12 -300 5/432   -300 1/9 -200 5/324 or            

200 5/36   -600 1/36 -400 5/1296   -500 1/18 -300 5/648   -400 1/12 -200 5/432 or            

300 5/36   -600 1/36 -300 5/1296   -500 1/18 -200 5/648 or            

400 5/36   -600 1/36 -200 5/1296Total       56,6%

Alea Iacta Est?

Normal distribution

Normal distribution

Project risk

Simple scenario Extended scenario Monte Carlo simulation

Simple scenario analysis

Example: 5 variables / 3 scenarios

Risk analysis ScenarioPrice per unit Negative Expected Positive

Selling price 8,00 10,00 12,00

Cost of raw materials 8,00 6,00 4,00

Cost of energy 2,00 1,50 1,00

Cost of labor 3,00 2,00 1,00

Result (5,00) 0,50 6,00

Units sold 1.000 2.000 3.000

Total result (5.000) 1.000 18.000

Extended scenario analysis

Example: 5 variables / 3 scenarios = 35 = 243 possible results

Monte Carlo Simulation

Known Knowns, Known Unknowns, Unknown Unknowns

Rumsfeld = Einstein

Known Knowns, Known Unknowns, Unknown Unknowns

="Not everything that counts can be counted, and

not everything that can be counted counts." (Sign hanging in Einstein's office at Princeton)

Black Swan events

0%

5%

10%

15%

20%

25%

30%

35%

40%

We

ek

ly g

row

th

Week

Growth Rate of a Christmas Turkey

Risk

Risk = Likelihood x Damage

Risk reporting

Probability classes Damage classes Risk scores Risk matrix

Example

Probability classes

Probability classes

Score Probability (%) Description Qualification

1 10% Less than 1 x per 3 years Very unlikely

2 30% between 1 - 2 x per 3 years Unlikely

3 50% between 2 - 3 x per 3 years Possible

4 70% between 3 - 4 x per 3 years Likely

5 90% More than 4 x per 3 years Very likely

Example

Damage classes

Damage classes

Score Damage Damage Qualification

1 Less than 100.000 EUR < 1% of equity Very low

2 between 100.000 - 400.000 EUR < 4% of equity Low

3 between 400.000 - 800.000 EUR < 8% of equity Serious

4 between 800.000 - 1.500.000 EUR < 15% of equity High

5 More than 1.500.000 EUR > 15% of equity Very high

Example

Risk scores

Event Effect Probability Damage Risk

External

Lower purchasing power Less demand, lower selling prices, higher bad debt levels

1 5 5

Inflation Higher raw material prices, labor costs, energy prices, transportation prices, interest costs

1 4 4

Shortage raw materials Higher raw material prices 2 3 6

Shortage labor Higher labor costs 1 1 1

Logistics interruption No supplies, no distribution 1 5 5

Example Risk matrix

High

Risk

Low R

isk

Risk

When the probability of an event is 100%, is it still risk?

Risico and behavior

After assessing the risks, how do you proceed?

Research Tversky / Kahneman (1979) Own experiment

Survey students period 1 - 2010/11

Situation 1:

100% certainty 3.000 EUR profit

vs

80% certainty of 4.000 and 20% certainty of zero profit

Situation 2:

100% certainty 3.000 EUR loss

vs

80% certainty of 4.000 and 20% certainty of zero loss

Results

Risk instruments

Legal:

Contracts

SLAs

Etc.

Economical:

Financial instruments (derivatives, insurance)

Internal control instruments

Etc.

Management:

Quality systems

HRM instruments: competencies, experience, motivation

Etc.

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