What is Value at Risk (VaR)?

Value at Risk (VaR) is a statistical measure that estimates the maximum expected loss of a portfolio over a specific time horizon at a given confidence level, typically 95% or 99%. For example, a 1-day VaR of 2% at 95% confidence means the portfolio should not lose more than 2% on any single day in 95 out of 100 days.

What is the difference between VaR 95% and VaR 99%?

VaR 95% measures the loss level exceeded only 5% of the time, while VaR 99% measures the loss exceeded only 1% of the time. VaR 99% is therefore more conservative and gives a higher loss figure, capturing more extreme scenarios. Risk managers often report both figures together.

What are the main methods for calculating VaR?

The three main methods are: Historical VaR (using actual past return distributions), Parametric VaR (assuming a normal distribution and using mean and standard deviation), and Monte Carlo VaR (simulating thousands of random scenarios). Historical VaR is intuitive and distribution-free; parametric is fast but assumes normality; Monte Carlo is the most flexible.

What is the main limitation of VaR?

VaR does not tell you how large the loss will be when it exceeds the threshold — it only tells you the threshold itself. For example, a 99% VaR of 5% means you know losses will exceed 5% on 1% of days, but not by how much. Expected Shortfall (CVaR) addresses this by measuring the average loss in the tail beyond VaR.

Value at Risk (VaR) 95% and 99%: practical guide

What VaR 95% and 99% mean

Value at Risk estimates the loss threshold that should not be exceeded over a chosen time horizon at a given confidence level.

$VaR_c = -Q_{1-c}(R)$

Example

If daily VaR 95% is 2.1% and VaR 99% is 3.4% on a $100,000 portfolio:

VaR 95% is about $2,100
VaR 99% is about $3,400

Historical vs parametric VaR

Historical VaR: uses empirical return quantiles.
Parametric VaR: uses distribution assumptions (often normal) with mean and volatility.

Historical is often more realistic in changing markets; parametric is faster for repeated scenarios.

Key limits

VaR does not describe the average loss beyond the threshold and can underestimate fat-tail events. Combine VaR with drawdown and CVaR analysis.

Next step

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Read related guides: Recovery Factor and Maximum Drawdown

Disclaimer

This article is not financial advice but an example based on studies, research and analysis conducted by our team.

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Value at Risk (VaR) 95% and 99%: practical guide

What VaR 95% and 99% mean

Example

Historical vs parametric VaR

Key limits

Next step

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Next step

Value at Risk (VaR) 95% and 99%: practical guide

What VaR 95% and 99% mean

Example

Historical vs parametric VaR

Key limits

Next step

Related guides

Portfolio rebalancing: when to do it and why it matters

Monte Carlo Simulation for your portfolio: a practical guide

TWR vs MWRR: which return metric should you trust?

Next step

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