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Monte Carlo Simulation for Risk Managers


Monte Carlo Simulation for Risk Managers

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									 Monte Carlo Simulation for Risk Managers
                Introduction and Model Risk Management
                                  MAFC Short Cours e Series 2010

Course Outline
Over the past 10 years, Risk Management has become a distinct function, recognised as essential to
the successful operation of large corporations, especially financial institutions. The precise roles and
responsibilities of risk managers, however, are still evolving, moving beyond the traditional quantitative
focus on measuring risk towards a broad discipline that now encompasses high-level managerial
concepts such as developing an enterprise wide 'risk culture'. Modern risk managers have to be multi-
disciplinary, understanding the complexities of risk analysis and aggregation in varied fast-moving
financial products and being able to construct risk management frameworks that not only 'manage' such
products but also comply with ever-changing regulatory requirements.
Today's risk managers need to have a knowledge and understanding of a wide variety of tools and
techniques that are used to measure risks in their organizations, so that they can judge if the tools are
being used appropriately. One of the key tools of modern finance is Monte Carlo Simulation (MCS),
which is used in fields as diverse as option valuation, insurance pricing, liquidity management and
operational risk capital calculation.
While risk managers do not need to be aware of the intricacies of building MCS models, they do have to
have a feeling of the 'model risk' involved. As the recent history of the Global Financial Crisis has
demonstrated, complex models can have serious limitations, which may only become apparent under
stress conditions. As a consequence, regulators are demanding that risk managers ask probing
questions of model developers to evaluate the appropriateness of the complex models used to manage
risk in their firms.

Who should attend
This course will be of interest to those who are faced with developing and implementing risk
management practices within large/medium organizations, especially those who deal with complex
financial models employing Monte Carlo Simulation. This includes not only members of risk
management functions but also other professionals, such as Compliance and Finance, who may be
responsible for monitoring and evaluating the results of complex risk models. The course will also be of
interest to those who are responsible for evaluating the effectiveness of risk management in a firm,
including auditors, actuaries, financial analysts and regulators.


Course Notes

Not necessary

Monte Carlo Simulation for Risk Managers [ver: 12/01/2010]                                        Page 1
Macquarie University Applied Finance Centre                         Short Course Series 2010

Patrick McConnell BSc., MSc., D.B.A, Dip. (Mgmt. Cons.), C. Eng., CITP,
FBCS, GAICD, Visiting Fellow (Macquarie Applied Finance Centre).
Dr. McConnell is a partner with Risk Trading Technology, a small consultancy
specialising in Risk Management and Information Technology. In over 30 years
in banking, insurance and commerce, Patrick has operated as a senior manager
and consultant in investment banks and large corporations in the US, Europe
and Australia. He is a published author and an experienced lecturer in risk
management, especially Operational and Enterprise Risk Management.

Monte Carlo Simulation for Risk Managers [ver: 12/01/2010]                            Page 2
Macquarie University Applied Finance Centre                              Short Course Series 2010

Detailed Outline
1.       Monte Carlo Simulation - Introduction
This section introduces concepts of Risk and Uncertainty and provides an overview of Monte Carlo
Simulation and it uses in modern Finance.
     What is Risk and Uncertainty?
     How is Risk Measured?
     Modelling Risk in Uncertain Environments
     What is a Model?
     What is Monte Carlo Simulation?
     History of Monte Carlo Simulation
     Use of Monte Carlo Simulation in Finance

2.       Model Risk and Regulation
This section describes the latest regulations on Operational Risk and Model Risk.
     What is Operational Risk?
     What is Model Risk?
     Regulations on Operational Risk
     Regulations on Model Development

3.       Examples of Model Risk Failure
This section provides examples of model failures.
     The Global Finance Crisis
     Examples of Operational Model Risk, e.g. NAB

4.       Statistics for Monte Carlo Simulation
This section introduces some statistical concepts and distributions used in Monte Carlo Simulation.
     Risk as a Distribution
     What is a Distribution?
     Distributions used in Finance
     Random and Quasi Random Numbers
     Fitting Data to a Distribution
     Q-Q/P-P Plots
     Good of Fit Tests
     Estimating Parameters for a Distribution

5.       Sample Model Case Study
This section describes a 'Sample Model' case study for which a MCS model will be built.
     Sample Problem
     Analysis of Uncertainty
     Possible Sources of Data
     Model Description
     Model Development (using MCS software)
     Running the Model

Monte Carlo Simulation for Risk Managers [ver: 12/01/2010]                                      Page 3
Macquarie University Applied Finance Centre                          Short Course Series 2010

6.       Model Development Process
This section describes how to develop a 'robust' model.
     Model Development Life Cycle
     Sensitivity Analysis
     Variance Reduction
     Model Communication

7.       Model Evaluation
This section discusses the issues of validating a model.
     Model Validation
     Model Documentation
     Back Testing - predicted versus actual

8.       Modelling Roles and Responsibilities
This section discusses roles and responsibilities for defining, developing and maintaining key risk
     Regulatory Requirements
     Modelling Roles: User, Developing, Validating
     Skills required for MCS Modelling
     Model Maintenance
     Role of Audit

Monte Carlo Simulation for Risk Managers [ver: 12/01/2010]                                 Page 4

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