How Does a Portfolio’s “Value at Risk” (VaR) Calculation Often Underestimate Tail Risk?

VaR is a statistical measure of the maximum expected loss over a set time horizon at a given confidence level (e.g. 99%).

It often underestimates tail risk because it typically relies on the assumption of a normal distribution, which has thin tails. Since financial returns, especially crypto, have fat tails (high kurtosis), VaR models fail to capture the true probability and magnitude of extreme, low-probability losses.

What Is the Main Challenge in Applying Standard VaR Models to Crypto Derivatives?
Why Does a “Jump Risk” Inherent in Crypto Assets Complicate VIX Calculation?
Why Is the Assumption of a Normal Distribution Problematic When Calculating HV for Crypto Assets?
What Are the Challenges of Using Historical Volatility Data for Margin in Crypto Derivatives?
Why Is the ‘Normal Distribution’ Assumption Sometimes Flawed for Crypto Volatility?
In Both Cases, Who Is the Party That Assumes the Risk?
What Percentage of Outcomes Fall within One Standard Deviation in a Normal Distribution?
How Is Value at Risk (VaR) Used in Setting Position Limits?

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