Measure and quantify various types of risk exposure with value-at-risk
Value-at-risk (VaR) is the risk measure that estimates the maximum potential loss of risk exposure given confidence level and time period. For example, a one-day 99% value-at-risk of $10 million means that 99% of the time the potential loss over a one-day period is expected to be less than or equal to $10 million. In other words, there is 1% chance that the potential loss over a one-day period will be greater than $10 million.
Value-at-risk is popularly used not only in risk reporting, but also in multiple phases of the risk management life cycle, including:
- Setting risk limits and risk budgets
- Computing regulatory capital requirements (e.g., Basel III, Solvency II)
- Backtesting value-at-risk models
- Calculating conditional value-at-risk, stress testing, and sensitivity analysis
Depending on the asset classes and types of risk exposure, risk managers employ various mathematical techniques to calculate value-at-risk, including:
- Monte Carlo simulation
- Copula-based portfolio simulation
- Pricing and valuation of financial derivatives
- Econometrics models (e.g., interest rate models and GARCH models)
For more information, see Statistics and Machine Learning Toolbox™, Financial Toolbox™, Financial Instruments Toolbox™, and Risk Management Toolbox™.
Examples and How To
See also: risk management, market risk, conditional value-at-risk, backtesting, Basel III, Solvency II, systemic risk, credit scoring model, concentration risk, portfolio optimization