Simulations in Financial Risk Management

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Use of Simulations in Financial Risk

Management

Rolf van der Meer

Crystal Ball Finance Workshop

Frankfurt am Main, 6 November 2006


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Page 2 Frankfurt am Main, 6 November 2006

Introduction

Use of Simulations in Financial Risk Management We will focus on the practical use of Monte Carlo

Simulations

A (fictional) case study will serve as background

Theory vs. practice?

Rolf van der Meer

Studies in Rotterdam (HES) and Durham, NC (Duke

University - Fuqua School of Business)

Leader of a team of consultants who advise corporationsand public sector entities on risk management issues


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Projected P&L of Berliner Maschinen AG over 2006(numbers in Mio; today is 31 December 2005)

TurnoverAluminium costOther raw materials

1,002.30304.59175.00

Gross margin 522.71

Personnel costOther costsDepreciation and amortization

Interest

200.00175.0070.00

17.64Target profit before taxes 60.07

Market price risks:

Charge to reflect aluminium forward curveHedging

-20.410.00

Operational risks:

Loss of an important customerCompensation for loss of an important customer

Damage to reputationMachine breakdown

0.000.00

0.000.00

Earnings (before taxes) 39.66

Taxes 13.88

Net earnings 25.78

Return on equity 13.2%

Case Study: Berliner Maschinen AG

fictional German supplier ofcomponents for carmanufacturers

Sales in Asia / Latin

America (10%/), US(20%/$), Europe (70%/)

Main raw materials input isaluminium (175,000t p.a.)

Total assets on the balance

sheet are 1bn, of which300m equity and 300minterest-bearing debt.


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The Risk Management Process

1. Fundamentalrisk strategy

2. Identification ofrisk exposures

5. Aggregation

3. Measuring riskexposures &building arisk model

4. Definition of risk taking andrisk retention strategies

6. Effectiveness

testing

7. Risk

monitoring


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1. Fundamental Risk Strategy

Objectives should fit into overall

strategy

create value (e.g. bypreventing bankruptcy)

eliminate costly lower-tailoutcomes while preserving

as much of the upside aspossible

Methodologies VaR, CaR, EaR, .

MCS at their best when itcomes to aggregating risksof different kinds (forinstance, market price risksand business or operationalrisks)

Berliner Maschinen AG:

Main objective: preserve equity base, avoid negative earningsMethodologies: EaR, Monte Carlo Simulation


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2. Identification of risk exposures

comprehensive risk landscape

collect risks in structured and systematic manner

Tools:

risk assessment workshop classification of risks in a probability/impact graph

brainstorming sessions

ask experts (inside & outside)

SWOT Porters Five Forces

checklists


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Risk Landscape of Berliner Maschinen AG

Category Risk factor DescriptionMarket risk Aluminium

priceHigher raw materials costs due to aluminium price rise on LME.

Market risk $/rate Value of exports in US depends on the $/rate.Aluminium price on LME is transferred intovia the $/rate.

Market risk Interest rate Possibly higher interest rate for debt after April 2006.

Credit risk Bad debts Not a major focus, because customers are large corporations thathave long business relationships with the company.

Operationalrisk

Machinebreakdown

If an important machine breaks down, the company incurs costsfor repairs and production delays.

Operationalrisk

Reputationdamage

Risk that deliveries are held up by a wastewater pipeline issue. Ifcustomers receive their components too late, the companys

reputation as a reliable supplier suffers.

Operationalrisk

Personnelcost

Personnel costs can fluctuate by about 2% due to uncertaintiesregarding remuneration and working times.

Business-volume risk

Loss of animportantcustomer

Sales contracts are long-term, but a customer may stop producinga car model for which the company supplies components.


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3. Measuring Exposures & Building a Model

market prices Markov process, random walk

does distribution fit empiricallyobserved market data?

relevant time horizon

fat tails

estimation by experts errors

heuristic biases

distinguish betweenprobability and impact

Unstable

30-days correlation between price changes

for 3-month aluminium contract (LME) and

dollar/euro exchange rate

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

4-Jan-2005

4-Feb-2005

4-Mar-2005

4-Apr-2005

4-May-2005

4-Jun-2005

4-Jul-2005

4-Aug-2005

4-Sep-2005

4-Oct-2005

4-Nov-2005

4-Dec-2005


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4. Risk Aggregation

Statistic Forecast

values

TrialsMeanMedian

ModeStandard DeviationVarianceSkewnessKurtosisCoeff. of VariabilityMinimum

MaximumMean Std. Error

10,00017.4123.22

---31.831,013.08

-2.5412.96

1.83-227.34

84.020.32

85% blue columns (positive net earnings)


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Relative importance of risk factors depends

on risk measure employed

Sensitivity chartContribution to variance of net earnings forecast.

c) = correlated; * = proceeds from goods originally destined for

lost customer

59%

13%

12%

10%

4%

2%

0%

0%

0%

Aluminium (1Q, 2Q, 3Q)

Loss of important customer (c)

Damage to reputation (c)

Machine breakdown (c)

Dollar/euro

Other raw materials

Personnel cost

Interest rate

Discount on open market*

Sensitivity chartContribution to variance of profit-or-loss forecast.

c) = correlated; * = proceeds from goods originally destined for

lost customer

36%

32%

18%

13%

1%

0%

0%

0%

0%

Loss of important customer (c)

Damage to reputation (c)

Aluminium (1Q, 2Q, 3Q)

Machine breakdown (c)

Dollar/euro

Other raw materials

Personnel cost

Interest rate

Discount on open market*


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5. Risk Strategies

Risk-taking

strategy Explanation Example for Berliner Maschinen AGRisk avoidance Renounce from risky

operationsSell in Latin America, but bill only in US dollarsand euros (and not in Latin American currencies).

Deliberate risktaking

Accept risks (possibly incombination with pricing

or diversificationstrategy)

Let US customers pay in US dollars (and use theresulting net hedge from long and short dollar

positionsee Hedging below).

Riskminimization

Minimize the likelihoodor impact of a risk factor(e.g. qualitymanagement)

Repair wastewater system.Ensure standby credit facilities.

Risk transfer Transfer risks to thirdparties (insurers, banks,suppliers, customers,etc.)

Insure against machine breakdown.Hedge aluminium price risk.


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6. Effectiveness Testing

aluminium price hedge hedge proposal of bank:

use natural hedgeopportunities, enter intoaluminium swap

left tail of the distribution isrelatively unchanged


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repair wastewater system reduce probability and

potential impact

higher expected value ofnet earnings

EaR decreases by 12.7m

Base Case Repair

Expected value(mean)Probability of lossEarnings at Risk

17.4 Mio

15%61.3 Mio

20.0 Mio

13%48.6 Mio


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ensure standby creditfacilities

need for standby credit toprevent liquidity shortage inworst case scenarios

simulation results show howmuch credit is needed indifferent percentiles


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7. Risk Monitoring

Risk Reporting gives precise view of the

risk structure of thecompany

shows deviations betweenthe firms actual risk

exposures and risktolerances (limits)

are targeted to readers

Risk Controlling critical ongoing appraisal of

the risk managementprocess

needs a risk managementfunction in the organizationthat lives

systematic approach

adequate measures aretaken shortly after a

warning level has beenreached

closed loop


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The good news

1. Adequate use of MCS improves forecasting qualityand provides better background for managementdecisions.

2. Inclusion of all risks (market prices, operationalrisks, event risks, etc.).

3. Systematic approach to value-oriented corporateplanning and controlling.

4. Thinking in ranges and probability distributionsenhances the acceptance of risk management.

5. Pragmatic risk models.


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Caveats & Side Effects

1. False impression of accuracy.

2. Dependence on the model used.Models cannot reflect the high level of complexityinherent in economic reality.Models are prone to errors (e.g. in Excel formulas).

3. Expert opinions are prone to errors, and may alsobe influenced by such psychological factors as

biases and company culture.4. Historical data may give false impressions.

5. Market prices are not normally distributed.


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Critical appraisal

MCS can greatly help acompany to cope withuncertainty.

Quantitative methods do not

make the future any morecertain, but they enablemanagers to make well-informed decisions.

Beware of unjustified sense

of control over uncertainty(reality is more complex).

For Berliner Maschinen AG Uncertainties can be better

measured

Transparent view of netearnings forecast, riskfactors

what-if analysis of riskstrategies proved veryworthwhile

better informational basisupon which to base riskmanagement decisions.

Simulations in Financial Risk Management

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