University of NSW

1. University of NSW Mark Young Head of Economic Capital and Portfolio Management Financial and Risk Management – Group Risk Management August 2005

2. Agenda A. Background of the Modern Financial Risk World • Applications of Mathematics in Financial Risk Management • Training the Next Generation of Financial Risk Mathematicians • Questions B. Fundamental Considerations for the Audience

3. A. Background of the Modern Financial Risk World

4. 1. Applications of Mathematics in Financial Risk Management A transforming world… • Risk management in the financial services industry has undergone a revolution. • The revolution has taken risk management from a control and over site function to that and more. • Risk is increasingly seen as an enabler to making new pricing and business growth decisions rather than acting as a barrier to doing business. • At its heart the measurement relies on some powerful and elegant mathematics. • It also provides a fertile ground for asking fundamental mathematical research questions that have clear and business relevant reasons for being addressed.

5. 1. Applications of Mathematics in Financial Risk Management Mathematics is one the foundations of the modern risk universe… • Banking as a first example, how do you quantify risk; what types of risk are they and how are they measured? • Insurance as another example what risks are there, are they different from banking? • Funds Management in some senses sits in the middle of these, does risk manifest itself in the same ways? • In overall sense they all face the same risk but its how they are interpreted that provides the difference.

6. 1. Applications of Mathematics in Financial Risk Management Different perspective but one common set of risks overall Credit Risk Operational Risk Market Risk Risk Universe Insurance Risk

7. 1. Applications of Mathematics in Financial Risk Management Credit Risk Regression Analysis, Data Mining and Business Judgements Keys to Understanding Gather historical data Segment credit (e.g., expected loss, Normalize and portfolio data (e.g., default frequency, Conduct analysis model data unrated, rated) severity (loss given default)) Option 1 Option 2 Output Utilize external data Utilize UBOC (e.g., loan pricing, portfolio data corporation, etc.) Unexpected Capital loss Combination • Without finely calibrated stochastic models its impossible to consider how banks might make estimates about loss. • Monte Carlo simulations provide important tools in unlocking any potential risks.

8. 1. Applications of Mathematics in Financial Risk Management Market Risk Statistics and Simulation are important clarifiers Portfolio Analysis Calculate Value at Risk, using appropriate models Identify sources of market risk in portfolio (eg. treasuryequities, options,pipeline) andA/L mismatch Translate Bank market A/L Mismatch VAR into risk VAR market risk measures Calculate value Duration gap at risk for report on bank present value charge in bank • Statistical Analysis provides insight in how to manage and interpret the complex interactions between market positions on different instruments. • Stochastic simulations of potential profit and loss outcomes help to inform business strategy.

9. 1. Applications of Mathematics in Financial Risk Management Operational Risk Extreme events and stochastic analysis of outcomes work hand in hand • Provides the greatest opportunity for fundamental questions in correlation analysis (copulas) and how they play in understanding risk analysis. • Opens the door to a new world of stochastic modelling and simulation techniques.

10. 1. Applications of Mathematics in Financial Risk Management Insurance Risk Based on established actuarial practice • Step 1: Gather policy level data • - Death benefit in the next year net of reinsurance • - Mortality rates, q, varying by age, sex, duration in force, etc. • Step 2: Mortality simulation • - For each policy, simulate a death/non-death based on a random variable X which is parameterised by the policyholder’s mortality rate q 0 if the policyholder is alive at the end of the year Xi (s) = 1 if the policyholder is dead at the end of the year • - Xi (s) has a Bernoulli ( ) distribution • - Measure the change in total mortality rates to understanding underlying risk

11. 1. Applications of Mathematics in Financial Risk Management Overall the Risks need to be seen as a portfolio and attributed to businesses Risk Building Blocks Model Risk Attribute Risk By Business Business Inputs Risk Building Blocks Model Risk Capital Risk Building Blocks Model Risk Capital Credit Risk Interest Rate Interest Rate Interest Rate Interest Rate Market Risk Foreign Exchange Foreign Exchange Foreign Exchange Foreign Exchange Equity Equity Equity Equity Commodity Commodity Commodity Commodity Market Risk Default Risk Default Risk Products Default Risk Default Risk Products Products t t t t Collateral Collateral Transactions Transactions Collateral Collateral Transactions Transactions Credit Risk t t t t Severity of Loss Severity of Loss Customers Customers Severity of Loss Severity of Loss Customers Customers t t t t Activities Activities Activities Activities t t t t Operational Errors Operational Errors Operational Errors Operational Errors P/L Restatements P/L Restatements P/L Restatements P/L Restatements Operational Risk Technology Investment Technology Investment Technology Investment Technology Investment Operational Audit Results Audit Results Audit Results Audit Results Risk Regulatory Flags Regulatory Flags Regulatory Flags Regulatory Flags Business Line Insurance Risk Insurance Risk

12. 2. Training the Next Generation of Financial Risk Mathematicians • Currently there exists a fragmented quantitative risk management educational environment globally. • Actuarial and Non Actuarial focused educational programs all stress the increasing importance of risk measurement and management. • The Charter Risk Analyst a new developing educational program in partnership with the Australian tertiary system and the Institute of Actuaries of Australia will: • Develop a designation to provide a focused quantitative risk management qualification.. • Provide the industry with the next generation of Risk Mathematicians.

13. 2. Training the Next Generation of Financial Risk Mathematicians Chartered Risk Analyst -Pathways Stage 1 Stage 2 Actuary & Chartered Risk Analyst Actuarial Part III pathway Part I Part II Inv Risk Measurement 6A Risk Practice 6B Cap + + = + + Delivered by University and the Institute Delivered by the Institute FIAA, CRA First Degree Undergraduate and or PostgraduateMathematics, Finance or Similar Master of Risk & Chartered Risk Analyst + = Master of Risk Risk Practice 6B Non Actuarial Delivered by University MRisk, CRA Delivered by the Institute Risk Measurement 6A Stage 1 Diploma of Risk Actuarial Risk Case Studies Is the staging ground to link actuarial and non actuarial knowledge to facilitate a transition to stage 2 Master of Risk Delivered by University and the Institute Diploma of Risk

14. B. Fundamental Considerations for the Audience

15. 3 Simple Questions • Honours or Pass …? • Career Planning…Why? • Do you want to use your degree…? August 2005

16. Get Ready for 5 Careers… FinancialServices Pharmaceuticals Teaching MarketingScience Government Applications

17. CASE STUDY No. 1 The Factory You are an Industrial Statistician/Mathematician working for Megacorp a large steel manufacturing company. You have, along with the production engineers been working on a new method of producing steel. It could save money and in turn help to employ 50 more people. You also stand to gain personally with a promotion to Chief Industrial Statistician/Mathematician of the new division if corporate management accept from your analysis that the results are favourable. You have been using a neural network (a type of nonlinear regression) model to forecast the output of quality of the new steel. More standard statistical and mathematical techniques do not have the elements that a neural network has to highlight the positives of this new method. Megacorp’s last quarter earnings were seriously down. Your manager warns you that if the results don’t show that the new process meets its promise not only will your other research grants be cancelled but you will be out of a job. Your worst fears are realised, the neural network shows unclear results on the new processes performance. You realise that if you re-interpret the data in a slightly different way that you can get a promotion, keep your current research grants and gain a fair amount of prestige from being on the team to pioneer this new method. Ethically some of the re-interpretation could be viewed as distorting the results. You feel uncomfortable about the re-interpretation. So what do you do? Options 1. I need that promotion. I’ll re-weight the figures. 2. I’ll tell management my concerns about needing more time 3. I’ll present the figures as I they are and let everything work itself out. August 2005

18. CASE STUDY No. 2 The Bible You’re involved in running a research study that is seen as the bible for the industry it services. The results in the final research will be mostly of a financial nature (revenues etc.). The final set of returns has been collected. Those that didn’t get collected by a certain date will be estimated. Part of the structure of the study’s process involves you working closely with a group of highly respected consultants. You had always assumed that their estimates were based on some complex mathematical model mixed with market intelligence. But the truth is far removed from this. It turns out that estimates are picked out of the air, normally done over a few beers. You are in two minds about what to do here. What do you do here? August 2005

19. CASE STUDY No. 3 The Offer You are a member of a small team of analysts who work in an “exclusive” market research firm. Your employers are great. You get on very well with your fellow analysts. The pay isn’t great but its okay, the work is boring but you have decided that it’s a great place to be while you look for another job. You chance upon seeing a job advertisement for a FMCG (Fast Moving Consumer Goods) company who are using the most advanced market research techniques and are looking for an analyst. You apply for it. You get the first interview. It turns out that the FMCG is a tobacco company. The pay is incredible, the opportunities in terms of networking you can get from working there are great and the chance of working overseas is a real possibility. You are offered the job. You’re not terribly keen on the people you have met there but the job seems a winner. Do you: 1. Take the job OR 2. Turn it down.

20. Questions