Value-at-Risk Analysis

Banks, central banks, sovereign and pension funds and corporate treasuries use Value-at-Risk to measure and limit market risk. Yet the figure is often taken at face value. Different methods give very different results for the same portfolio, historical windows miss regime changes, correlations break down in crises, and VaR says nothing about how large a loss could be once the threshold is crossed. Boards and committees receive VaR numbers without understanding their assumptions, and limits are set without a clear link to risk appetite.

This programme builds VaR from first principles to decision use. It starts with market risk measurement and the statistics behind it, then compares the main calculation methods, moves to expected shortfall, stress testing and backtesting, and finishes with the use of VaR in limits, capital, investment management and governance. Each method is worked through on realistic portfolios so participants see how assumptions drive results.

Built on recognised practice. The programme references the market risk framework of the Basel Committee on Banking Supervision, including backtesting principles and the move from VaR to expected shortfall under the Fundamental Review of the Trading Book, the RiskMetrics approach to volatility estimation, established statistical backtesting tests, and the market risk sensitivity disclosure requirements of IFRS 7. Regulatory material is used to explain good practice, not as a compliance manual for any single jurisdiction.

Decisions this programme improves. Which VaR method suits a given portfolio; which confidence level, horizon and data window to choose; whether a model is performing adequately or needs recalibration; how to set and allocate risk limits; when stress tests should override VaR in decisions; and how to present risk measures to committees in a way that supports action.

How it is delivered. Twenty hours across five sessions, built around one running case: a multi-asset portfolio of bonds, equities, foreign exchange and simple derivatives managed by a treasury or reserve management team. Participants calculate VaR with several methods in spreadsheet models, backtest the results, run stress scenarios and prepare a risk report for an investment committee.

In-house option. For organisations, the programme can be tailored to your own portfolios, risk systems, limit framework and regulatory environment, and delivered to risk, treasury, investment and audit teams together so that everyone interprets the numbers the same way.

Who Should Attend

Risk professionals: market risk managers, risk analysts and heads of risk in banks and financial institutions

Treasury and investment staff: treasurers, portfolio managers and reserve managers in central banks, funds and corporates

Model validation and quantitative staff: analysts responsible for building or reviewing risk models

Audit and supervision staff: internal auditors and banking supervisors who review market risk frameworks

Finance professionals: financial controllers and analysts responsible for risk reporting and disclosure

Objectives

Explain what VaR measures, what it does not measure and how confidence level and horizon affect it

Estimate volatility and correlation using historical and exponentially weighted methods

Calculate VaR using the parametric, historical simulation and Monte Carlo methods and compare their results

Apply VaR to portfolios with bonds, equities, foreign exchange and options, including non-linear exposures

Decompose portfolio VaR into marginal, incremental and component contributions

Calculate expected shortfall and explain why regulators prefer it for tail risk

Design stress tests and scenario analyses that complement VaR

Backtest a VaR model with statistical tests and interpret exceptions

Set and monitor VaR-based limits linked to risk appetite

Present VaR and related measures clearly to senior management and committees

Course Outline

Measuring Market Risk: Foundations of VaR

Market risk sources: interest rates, equities, foreign exchange and commodities
Returns, volatility, distributions and fat tails
Definition of VaR: confidence level, holding period and base currency
Volatility estimation: equally weighted, EWMA and GARCH approaches
Correlation, diversification and its limits in stressed markets
Scaling VaR across horizons and its assumptions

Calculating VaR: Methods and Choices

Parametric (variance-covariance) VaR and risk factor mapping
Historical simulation, including filtered and weighted variants
Monte Carlo simulation: design, run time and accuracy
VaR for fixed income: duration, convexity and key rate exposures
VaR for options and non-linear positions: delta, gamma and full revaluation
Comparing methods on the same portfolio: why results differ

Beyond VaR: Tail Risk and Stress Testing

Limitations of VaR: tail blindness and lack of subadditivity
Expected shortfall: calculation and interpretation
Stressed VaR and the choice of stress periods
Historical and hypothetical stress scenarios; reverse stress testing
Liquidity-adjusted VaR and concentration risk
Credit VaR and an introduction to portfolio credit risk models

Validating and Governing VaR Models

Backtesting: exception counting, the traffic light approach and statistical tests
Independence and conditional coverage tests
Model risk: data quality, proxies and parameter choices
Model validation, documentation and independent review
The regulatory perspective: Basel market risk framework and the FRTB
Emerging uses of machine learning in risk estimation: potential and limits

Using VaR in Decisions: Limits, Capital and Reporting

Linking risk appetite to VaR and stress loss limits
Marginal, incremental and component VaR for risk allocation
Risk-adjusted performance measurement
VaR in reserve management, asset management and corporate treasury
Disclosure of market risk under IFRS 7
Risk reports and dashboards for boards and committees
Action plan: strengthening market risk measurement in your organisation

Competencies

Market risk measurement fundamentals

Volatility and correlation estimation

Parametric, historical and Monte Carlo VaR

VaR for fixed income and options portfolios

Expected shortfall and tail risk analysis

Stress testing and scenario analysis

VaR backtesting and model validation

Risk limit setting and risk allocation

Market risk reporting for senior management

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