Backtesting exceções VaR
- Created by
- Renato Passos, Eng. de Software
- Reviewed by
- Renato Passos, Eng. de Software
Last updated: Apr 18, 2026
About this calculator
The VaR exceptions backtesting calculator helps assess the accuracy of financial risk models. It compares the number of actual exceptions (real losses exceeding VaR) with the expected value, calculated as N · (1 − confidence level). This identifies whether the model underestimates or overestimates risks.
The formula N · (1 − level) shows the theoretical number of exceptions expected in a period, based on the chosen confidence level (e.g., 95% or 99%). If the actual exceptions exceed this significantly, the model may be miscalibrated, requiring adjustments to avoid risk overestimation.
This tool is critical in risk model audits, especially after financial crises or portfolio changes. Analysts and regulators use it to ensure financial institutions comply with risk management standards like Basel or BACEN regulations.
Key considerations: historical data must be representative, and models shouldn't rely solely on exceptions. Factors like volatility and asset correlations must also be analyzed for a comprehensive evaluation.
Frequently asked questions
What is a VaR exception?
An exception occurs when real portfolio losses exceed the Value at Risk (VaR) threshold for a given confidence level.
How does the calculator determine expected exceptions?
It uses the formula N · (1 − confidence level), where N is the number of observations and the confidence level is the probability of VaR not being exceeded.
What data is required to use this tool?
You need the total number of observations (N) and the VaR model's confidence level (e.g., 95% or 99%).
How do you know if a VaR model is well-calibrated?
If actual exceptions are close to the expected number, the model is well-calibrated. Excessive exceptions indicate risk underestimation.
Are there limitations to this backtesting method?
Yes, it doesn't account for changes in asset correlations or extreme events not present in historical data.