Quantifying potential losses at a given confidence level
Value at Risk (VaR) answers a simple question: "What is the most I can expect to lose over a given period at a given confidence level?" For example, a 1-day 95% VaR of $1M means there is a 5% chance of losing more than $1M in a single day.
Formally, VaR at confidence level is the -quantile of the loss distribution:
Assume returns are normally distributed with mean and standard deviation :
where is the standard normal quantile (e.g., , ).
For a portfolio with value :
Sort the last daily P&L observations. The VaR at 95% is the 5th percentile of that empirical distribution. No distributional assumptions needed, but it relies on the past being representative of the future.
Simulate thousands of scenarios from a fitted model, compute the portfolio P&L for each, and take the appropriate quantile. Most flexible — handles non-linearities, fat tails, and complex portfolios.
Under the i.i.d. assumption, scale from 1-day to -day VaR:
This rule comes from the CLT and is widely used despite its limitations (autocorrelation, volatility clustering).
A portfolio has daily returns with and . Portfolio value is $10M. Compute the 1-day 99% VaR.
There is a 1% chance of losing more than $349K in a single day.
Interview tip: Always mention VaR's limitations unprompted. Interviewers want to see you understand it's a flawed but practical tool, not a complete risk measure.
VaR: The loss that is exceeded with probability . "With 99% confidence, we won't lose more than $X."
Expected Shortfall (CVaR): Average loss given VaR is breached. .
ES is coherent (subadditive: diversification always reduces risk). VaR is not — it can penalize diversification.