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MAS Finance meets Bank Julius Baer Presentation of B. Hodler/N. MacCabe April 2, 2004

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Agenda Julius Baer Group Risk management organisation Risk landscape Working with a MAS Finance intern: a case study Questions / Discussion

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Julius Baer Group (figures in Mio CHF) Assets under Mgt115,500 49,400 Net operating income 1, Net profit Equity 1,474 1,164 Capitalization 4,282 1,514 Headcount 1,766 1,470 ROE 5.3 % 10 %

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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

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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)

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Group Risk Management B. Hodler, CRO A. Weber, Deputy Credit Risk A. Weber Private Banking D. Münchbach IT & Operations U. Läderach / Ph. Malherbe J. Hüsler Trading R. Winkler GRM NY HR Würgler Relationship Mgt K. Schmid Market Risk S. Altner Operational Risk B. Hodler Asset Mgt & Funds B. Briner Risk Advisory N. MacCabe Support M. Calpini Risk management organisation

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Julius Baer Group Risk Landscape

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Six 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

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Case 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

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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

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Problems 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

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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)

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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

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Key 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.

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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 perspective

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Intern‘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

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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: ExpChange t = ESH t = IssuerSpread + Current RFR*ExpChange t GroupProbability AAA AA A Bond: 3.75 Akademiska 06 TTM: 2.09 years IS: bp

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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.

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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.

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