Course Background
Since the early 1990s, Value-at-Risk has become an increasingly popular methodology for the measurement and reporting of risk, especially within banks. The Market Risk Amendment of the Basel Accord, introduced in 1995, permitted the use of VaR to set regulatory capital for market risk. More recently, the new Incremental Risk Charge proposed by the Basel Committee requires banks to estimate much more stringent VaR estimates across a wide range of market risk factors.
Summary of course content
- Understand why has Value-at-Risk become the accepted methodology for assessing risks across the world
- What are the popular measurement methodologies for market, liquidity, credit and operational risks on a portfolio basis. Computer-based demonstrations will be given, and delegates will do a range of exercises on each of them.
- How VaR may be used to set risk appetite, how this appetite may be allocated downwards, and whether VaR may be used as a control mechanism. One fundamental question here is whether the diversity benefit is allocable?
- How VaR may be used as a reporting mechanism. What would go into a good VaR report for senior management?
- What is the interaction between VaR and other risk measures such as market risk sensitivities?
- Has VaR had a good economic crisis? What lessons may be learnt?
- Why VaR needs to be supplemented by well-designed stress tests? What is the concept of Stress VaR, which has been recently introduced into the Basel Accord?
Methodology
This course is very interactive, combining formal lectures with practical sessions, discussions and a wide range of computer-based exercises which can be taken away after the course. This will reinforce your learning and ensure that you are ready to apply the course as soon as you return to your institution.
Who should attend this training course?
- Senior management, including line managers
- Risk managers/consultants
- Financial controllers
- Senior back office people
- IT developers
- Audit, both internal and external
Supporting publications
DAY ONE
Introduction
- What is VaR - example based upon a credit portfolio?
- Why has VaR become the accepted methodology for assessing risk?
- Can it be applied to all risks?
- Has VaR performed successfully in 2007-8? Can it be applied in periods of stress?
Market risk VaR - I
- Estimation of 1-factor VaR of a simple trading example using historic simulation
- Changing the confidence level and time horizon - some basic theory
- Extension of the example to 2-factors
- Demonstration of a large 8-factor example
- Estimation of the diversity benefit - marginal VaR
- What do banks do in practice - an examination of some selected bank practices
- Difficulties and extensions with historic simulation
- Use of international data
- How to improve the accuracy of HS - outlining the use of extreme value theory
- Expected tail losses and other risk measures
- Use of structured forecasting - ARIMA and other methods
Computer-based exercise: measure the VaR of a traded portfolio, and explore the impact of various extensions
Market risk VaR - II
- Development of a parametric delta-approximation (also known as VCV) methodology
- Applying this methodology to the 8-factor example above
- Aside: the RiskMetric methodology - a brief outline
- Extensions to the parametric method
- Improved estimation of volatilities and correlations
- Use of PCA to reduce the number of risk factors - building PCA VaR
- Application of the parametric approach to an option portfolio
- Development of a delta-gamma approach to the option portfolio
- Calculation and decomposition of credit-spread VaR
- Use of beta analysis to include equity and equity indices
Computer-based exercise: measure the parametric VaR of the same traded portfolio
DAY TWO
Market risk VaR - III
- Measuring VaR by Monte-Carlo simulation
- Generation of random scenarios
- Estimation of VaR
- Applying this methodology to the 8-factor example above
- Applying this methodology to the above option portfolio
- How to speed up the simulation - delta-gamma simulation
- How to improve the accuracy of the simulation by better sampling
- Advantages and disadvantages of MC simulation
Computer-based exercise: measure the MC VaR of the same traded portfolio
Credit risk VaR
- Modelling a credit-sensitive portfolio by Monte Carlo simulation, including the Incremental Credit Risk (ICR), the Counterparty Credit Risk of default or downgrade (known as Credit Valuation Adjustment - CVA)
- Brief overview: estimation of basic input parameters such as EADs, PDs, LGDs
- Building a loss distribution by simulating defaults
- Estimation of default correlations
- Building a value loss distribution by simulating migration
- Building a loss distribution by using a simulationbased copula methodology
Computer-based exercises: for a given credit-sensitive portfolio, estimate the loss distributions using the various approaches
- Semi-analytic approaches: how to model a well diversified portfolio analytically
- Calculating the VaR for a large portfolio
- The derivation of the regulatory charge for credit risk
Computer-based demonstration: pricing iTraxx tranches
DAY THREE
Operational risk VaR
- Overview of the two main bottom-up approaches for operational risk
- Historic analysis - also known as Loss Distribution Approach (LDA)
- Fitting distributions to frequency and severity
- Building a loss distribution by simulation
- Estimating VaR by using EVT
- Control Self Assessment (CSA)
- Overview of the methodology
- Real case-study based on work for a large European bank
- Building a loss distribution by simulation
- Estimating VaR by using EVT
Computer-based exercises: estimate operational VaR based upon some real bank data
Use of VaR for control and reporting
- Setting of risk appetite
- What are the main contributory factors to a risk appetite?
- How are risk appetites expressed?
- How can risk appetites be allocated downwards?
- Should the diversification benefit be taken into account?
- Using VaR for low level control
- Can VaR be used for control?
- Advantages and disadvantages
- Using VaR for high level reporting
- What should go into a high level VaR report?
- Example of a real VaR report to a Board Stress testing or "when does VaR break down?"
- What happened in 1998 and in 2008 - did VaR deliver?
- What happens in times of stress?
- What is the purpose of stress testing?
- How to conduct stress tests?
- What are the main management messages from stress testing?
- How does stress testing and VaR integrate?
- Introduction of the incremental risk charge into the Accord
Summary of course
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Dr. Richard Flavell
Richard Flavell
Dr Richard Flavell is a consultant in the financial services industry. Until recently, he was Director of Financial Engineering at Lombard Risk Systems, one of the leading providers of derivative trading systems around the world. In this role he led a team responsible for the mathematical development of Lombard’s derivative trading and risk management systems. At the same time, he also undertook extensive client/product training and consultancy projects.
Prior to his role at Lombard Risk, Dr Flavell was Head of Financial Engineering at ANZ Merchant Bank in London, and was Reader in Finance at The Management School, Imperial College, which is part of the University of London. He has worked with many banks and financial institutions around the world, advising them on their derivative and risk management activities. Dr Flavell has an international reputation for his expertise in swaps, other derivatives and risk management.
Dr Flavell has also published widely in both academic and professional literature, his most recent book on Swaps and other Derivatives was published in December 2009, and he is currently writing a book on bank risk management. His approach to training is structured and practical. He has extensive experience and success in teaching both recent entrants to the derivatives markets and risk management, as well as highly experienced technical experts and market participants.
Courses run by this instructor
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