MAS Finance meetsBank Julius Baer

Presentation of

B. Hodler/N. MacCabe

April 2, 2004

Agenda

Julius Baer Group

Risk management organisation

Risk landscape

Working with a MAS Finance intern: a case study

Questions / Discussion

Julius Baer Group (figures in Mio CHF)

Assets under Mgt 115,500 49,400

Net operating income 1,020 525

Net profit 82 113

Equity 1,474 1,164

Capitalization 4,282 1,514

Headcount 1,766 1,470

ROE 5.3 % 10 %

2003 1995

Julius Baer Group

Private Banking

Asset Management and Funds

Trading

Corporate Center

Risk Management

Finance and Controlling

Legal and Compliance

IT and Operations

Communication

Human Resources

Investment Research

Risk management organisation Risk management organisation Risk management organisation Risk management organisation

Board of Directors committees:

Risk committee of the board (quarterly)

Audit committee of the board (quarterly)

Executive Board committees:

Group ALM committee (monthly)

Group risk committee (weekly)

Group lead management committee (on request)

Group Risk Management

B. Hodler, CROA. Weber, Deputy

Credit RiskA. Weber

Private Banking D. Münchbach

IT & OperationsU. Läderach /Ph. Malherbe

J. Hüsler

TradingR. Winkler

GRM NYHR Würgler

Relationship Mgt

K. Schmid

Market Risk S. Altner

Operational Risk

B. Hodler

Asset Mgt & Funds

B. Briner

Risk AdvisoryN. MacCabe

SupportM. Calpini

Risk management organisation Risk management organisation Risk management organisation Risk management organisation

Julius Baer Group Risk Landscape

Market Risk

Credit Risk

Strategic / Business Risk

Operational Risk

Funding / Liquidity Risk Fraud

Clients & products

System & physical risk

Execution, delivery & process

Personnel

Legal & tax liability / default

Reputational Risk

Six commandments of risk managementSix commandments of risk management

Foster risk and return awareness Understand your profits Be prepared to pay Reconcile with diligence (and on time) Track the cash Watch your systems

Case studyCase study

Finance practitioners and academia working together

Project to model issuer specific risk on non-government bonds at Julius Baer

What is issuer specific risk?

Key advantages of approach taken

The practitioner’s perspective

The intern’s perspective

What is issuer specific risk?What is issuer specific risk?

Risk from changes in price of a bond NOT due to changes in the risk-free rate of interest

Issuer-specific risk (ISR) present in all non-govt bonds

Comparable magnitude to pure interest rate risk – can be much larger

Modelling pure IR risk fairly easy

Modelling ISR much harder

Problems with modelling ISRProblems with modelling ISR

Reliable historic prices are not available for most bonds

Even if they were available they would be of limited use because time to maturity of a bond changes every day

Theoretically, problem 2 could be resolved by building a yield curve (based on numerous bonds) for each issuer. Very difficult in practice and very time consuming.

An approach based on the rating (S&P, Moody’s) of a bond could be used, but this presents numerous difficulties too

How did Enrique model ISR?How did Enrique model ISR? Measured spread of each bond (at current market price) over

risk free rate at same time to maturity (TTM)

Captured not only risk free yield curve for each currency, but also various rating specific yield curves per currency (from Bloomberg)

Took the interpolated spread over the risk free yield curve at each TTM and for each rating specific curve

At each TTM calculated the historic volatility of these various rating specific yield curves

Used discriminant analysis to determine probability that each bond’s spread would fall into a given rating category (usually several probabilities, summing to one)

How did Enrique model ISR? (2)How did Enrique model ISR? (2) Constructed an expected spread history for each bond (based

on historical spreads of each rating category and posterior probabilities)

Once the expected spread history was calculated, GARCH was used to find the best fit for the time series. These then drove simulated paths for the expected spread history. This had effect of rewarding diversification.

All of this was then automated in a routine using the SAS statistical package

Key advantages of this approachKey advantages of this approach

Rewards diversification

Backtesting against actual bonds (with reliable history) shows model makes good estimates

No additional data on individual bonds needed

Can deal with any bond

Routine chooses best GARCH model for each bond‘s expected spread history

Because main input is bond‘s current spread, model reacts immediately to changes in market perception of an issue‘s credit quality.

Assign one clearly defined task only to the intern

Task should require developing new approach to some problem (e.g. a modelling problem)

If modelling involved, define an approach to backtesting early on

Recognise you are taking a risk

Encourage intern to attempt multiple approaches (unlikely to be right first time)

Review progress regularly (at least once a week)

Be prepared to spend time helping the intern

Ensure intern has time to write thesis.

Financial practitioner‘s perspectiveFinancial practitioner‘s perspective

Intern‘s perspectiveIntern‘s perspective

Ensure task is clearly defined and that you understand it

Ask yourself seriously if you have what it takes to do the job

Try to gauge whether the task is doable in the time

Find out who your supervisor will be and make sure you spend time talking to them about project. Can you work with them?

Ask how much time your supervisor will be able to spend with you.

Ensure you have time for writing your thesis

Expected Spead History Calculation

Position‘s Rating may change during its lifetime. Thus, given position‘s current YTM, a Discriminant Analysis was performed using the simulated changes

Probabilities of „membership“ into each Rating Category are obtained and these are used to construct an Expected Spread History (ESH) as follows:

C

1it,ii ChgCat*CategoryadIssuerSprePExpChanget =

ESHt = IssuerSpread + Current RFR*ExpChanget

Group Probability

AAA 0.0000

AA 0.6224

A 0.3776

Bond: 3.75 Akademiska 06TTM: 2.09 yearsIS: 19.17 bp

Monte Carlo Simulation and Risk Measures Calculation

Using Monte Carlo, two bonds with exactly the same TTM and YTM will have different simulated spreads. In this way, the ESH of this simulated paths will not be perfectly correlated and diversification reward is attained.

For each trading day, a random number from a (0, t ) is

drawn. The simulated pahts consider the volatility‘s time dependence.

Changes in the PV of the position is calculated using the Simulated Spread.

Backtesting

Some bonds issue in CHF were selected with its price past history, and a daily HSVaR was computed for the last 210 days.

Changes in bond‘s price due Issuer Spread is isolated and compared with the HSVaRs.

HSVaR99 HSVaR95

Exceptions Simulation ExpSpread Simulation ExpSpread # days

Observed 1 2 5 12 210 Expected 2 2 10 10 Rabobank

% 0.50% 1.00% 2.40% 5.70% Observed 1 2 3 8 210 Expected 2 2 10 10 Hessen

% 0.50% 1.00% 1.40% 3.80% Observed 1 5 6 19 210 Expected 2 2 10 10 General Motors

% 0.50% 2.40% 2.90% 9.00% Observed 1 2 3 10 210 Expected 2 2 10 10 BP Amoco

% 0.50% 1.00% 1.40% 4.80% Observed 0 1 2 5 210 Expected 2 2 10 10 Roche

% 0.00% 0.50% 1.00% 2.40% Observed 0 0 0 1 170 Expected 2 2 8 8 Gemeenten

% 0.00% 0.00% 0.00% 0.60%

Observed 6 7 13 24 210 Expected 2 2 10 10

Electricite de France

% 2.90% 3.30% 6.20% 11.40%