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University of NSW . Mark Young Head of Economic Capital and Portfolio Management Financial and Risk Management – Group Risk Management August 2005. Agenda . A. Background of the Modern Financial Risk World Applications of Mathematics in Financial Risk Management

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university of nsw

University of NSW

Mark Young

Head of Economic Capital and Portfolio Management

Financial and Risk Management – Group Risk Management

August 2005

agenda
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

1 applications of mathematics in financial risk management
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.
1 applications of mathematics in financial risk management1
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.
1 applications of mathematics in financial risk management2
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

1 applications of mathematics in financial risk management3
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.
1 applications of mathematics in financial risk management4
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.
1 applications of mathematics in financial risk management5
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.
1 applications of mathematics in financial risk management6
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
1 applications of mathematics in financial risk management7
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

2 training the next generation of financial risk mathematicians
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.
2 training the next generation of financial risk mathematicians1
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

slide15

3 Simple Questions

  • Honours or Pass …?
  • Career Planning…Why?
  • Do you want to use your degree…?

August 2005

slide16

Get Ready for 5 Careers…

FinancialServices

Pharmaceuticals

Teaching

MarketingScience

Government

Applications

slide17

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

slide18

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

slide19

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.