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Risk & Portfolio

Value at Risk

Quantifying potential losses at a given confidence level

7 min read1 practice problems1 theory cards

What Is VaR?

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 α\alpha is the α\alpha-quantile of the loss distribution:

P(L>VaRα)=1α.P(L > \text{VaR}_\alpha) = 1 - \alpha.

Three Approaches

Parametric (Variance-Covariance)

Assume returns are normally distributed with mean μ\mu and standard deviation σ\sigma:

VaRα=(μzασ)\text{VaR}_\alpha = -(\mu - z_\alpha \, \sigma)

where zαz_\alpha is the standard normal quantile (e.g., z0.95=1.645z_{0.95} = 1.645, z0.99=2.326z_{0.99} = 2.326).

For a portfolio with value VV:

VaRα=V(zασΔtμΔt).\text{VaR}_\alpha = V \cdot (z_\alpha \, \sigma \sqrt{\Delta t} - \mu \, \Delta t).

Historical Simulation

Sort the last nn 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.

Monte Carlo

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.

Scaling VaR

Under the i.i.d. assumption, scale from 1-day to TT-day VaR:

VaRT=VaR1T.\text{VaR}_T = \text{VaR}_1 \cdot \sqrt{T}.

This T\sqrt{T} rule comes from the CLT and is widely used despite its limitations (autocorrelation, volatility clustering).

Worked Example

A portfolio has daily returns with μ=0\mu = 0 and σ=1.5%\sigma = 1.5\%. Portfolio value is $10M. Compute the 1-day 99% VaR.

VaR99%=10,000,000×2.326×0.015=348,900.\text{VaR}_{99\%} = 10{,}000{,}000 \times 2.326 \times 0.015 = 348{,}900.

There is a 1% chance of losing more than $349K in a single day.

Limitations

  • VaR says nothing about how bad losses can be beyond the threshold.
  • It is not subadditive — combining two portfolios can increase VaR, violating the diversification principle.
  • These limitations motivate Expected Shortfall (next lesson).

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.

Key Concepts

Value at Risk & Expected Shortfall

VaRα_\alpha: The loss that is exceeded with probability α\alpha. "With 99% confidence, we won't lose more than $X."

Expected Shortfall (CVaR): Average loss given VaR is breached. ESα=E[LL>VaRα]ES_\alpha = E[L \mid L > \text{VaR}_\alpha].

ES is coherent (subadditive: diversification always reduces risk). VaR is not — it can penalize diversification.

VaRα=F1(1α)\text{VaR}_\alpha = F^{-1}(1 - \alpha)

ESα=E[LL>VaRα]ES_\alpha = E[L \mid L > \text{VaR}_\alpha]