What is Value at Risk (VaR)?

Gurdip Dhami

25 years: Treasury & ratings

Discover how Value at Risk (VaR) measures potential portfolio losses over time. Learn the key inputs such as exposure, time horizon, confidence level, volatility and distribution, and how VaR supports market risk management.

Treasury Risk Management

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What is Value at Risk (VaR)?

10 mins

Key learning objectives:

Understand Value at Risk (VaR) and understand its purpose in measuring market risk
Identify the five primary inputs required to calculate VaR
Explain the concept of confidence levels, time horizons, and volatility in VaR estimation

Overview:

Value at Risk (VaR) measures the maximum expected loss on a position or portfolio over a chosen time horizon and confidence level, focusing specifically on downside risk. It relies on five core inputs: the size of the exposure, the holding period, the confidence level, the volatility of underlying market variables, and an assumed probability distribution of price changes. VaR can be calculated using different approaches ranging from simple historical analysis to model-based simulations that incorporate changing market dynamics.

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Summary

What is Value at Risk and why is it important?

Value at Risk (VaR) quantifies the maximum potential loss a portfolio or exposure may experience over a specified time period and confidence level. It provides a single, comparable measure of market risk that helps organisations assess whether they are comfortable with their current exposure or need to hedge. VaR is critical for risk managers, regulators, and investors because it translates complex risk data into a clear metric for setting limits, allocating capital, and managing performance under uncertainty.

What are the key inputs required to calculate VaR?

Exposure size: The amount or portfolio under risk
Time horizon: The period over which risk is assessed
Confidence level: The probability that losses will not exceed VaR
Volatility: The variability of market prices or rates
Probability distribution: The model for how those changes occur

Each factor influences VaR magnitude: larger exposures, longer horizons, and higher volatility increase risk estimates. The chosen distribution, often a normal distribution, but not always appropriate, determines how tail risks are represented.

What assumptions and limitations should users of VaR be aware of?

VaR models rely on historical or simulated data, which may not capture extreme or unprecedented events. They assume that past volatility patterns and relationships remain stable, an assumption that can break down during periods of crisis. VaR also focuses on expected maximum loss at a given confidence level; it does not reveal the size of losses beyond that point. For this reason, it should be complemented with stress testing, back-testing, and scenario analysis to account for tail risk and structural changes in the market.

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Gurdip Dhami

Gurdip has over 25 years of experience in the financial services industry. He has had roles in corporate treasury, risk management, debt capital markets, debt advisory and credit ratings advisory. During this time Gurdip has worked at Standard Chartered Bank, Bank of America, JPMorgan, Resolution Life, and NatWest Markets. He has a BSc in Physics and MSc in Operational Research.

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