Value at Risk (VaR)
A statistical estimate of the worst expected loss over a defined horizon at a chosen confidence level.
Definition
Value at Risk (VaR) is a risk measure that answers one practical question: "What is the maximum loss I should expect on this position or portfolio over the next N days, with X% probability?" For example, a 1-day 99% VaR of ₹50,000 means that on 99 out of 100 trading days, the loss is expected to be below ₹50,000 — and on roughly one day in a hundred it may exceed that. VaR is widely used by institutional traders, risk managers, and regulators because it compresses complex multi-asset risk into a single rupee figure. In the Indian context, NSE directly uses VaR to set cash-market margin requirements, making it one of the most operationally relevant risk concepts for retail traders holding stock futures or large equity positions.
Why it matters
Understanding VaR gives traders a framework for sizing positions consistently rather than relying on intuition. If your total account is ₹10 lakh and your portfolio's daily 99% VaR is ₹1 lakh, you are risking 10% of capital on a single bad day — an exposure level many risk frameworks would flag as excessive. NSE publishes VaR margins for individual stocks in the cash segment; these margins can jump sharply after a stock enters the F&O ban period, breaches its MWPL, or is placed under ASM/GSM surveillance. For F&O traders, VaR also explains why brokers impose additional "ad hoc" margins during earnings seasons or budget days — the exchange raises the VaR multiplier on illiquid or high-beta scrips, and brokers pass that on. Traders who understand VaR can anticipate margin calls rather than being surprised by them.
Formula
There are three common approaches. The parametric (variance-covariance) method assumes normal returns:
VaR = −(μ − z × σ) × Portfolio Value
where μ is the mean daily return, σ is the daily standard deviation, and z is the z-score for the chosen confidence level (2.33 for 99%). Historical simulation replays actual past returns ranked from worst to best, taking the return at the 1st percentile. Monte Carlo simulation generates thousands of random paths calibrated to estimated volatility. NSE uses an exponentially weighted variant of historical simulation that gives more weight to recent observations, so the VaR margin rises quickly when historical volatility spikes and falls gradually as markets calm.
Example
Suppose you hold 500 shares of a mid-cap stock trading around ₹400 each, giving a position value of ₹2,00,000. NSE's VaR margin for this stock is currently 15% (reflecting its higher volatility). The VaR margin you must maintain is 0.15 × ₹2,00,000 = ₹30,000. If the stock gets placed under ASM Stage II and the VaR margin is raised to 40%, your required margin instantly becomes ₹80,000 — a ₹50,000 shortfall if you haven't kept the buffer. This is a real and common scenario for traders who buy high-beta mid-cap stocks on margin near results season. The lesson: check the current VaR margin before entering, not after.
Stay ahead of margin changes
TradePulse surfaces OI and volatility signals that often precede margin hikes — know before your broker calls.