Measure intraday downside risk with flexible inputs quickly. Switch models and confidence for quick comparisons. Make faster decisions during volatile sessions with clearer limits.
Intraday Value at Risk turns high‑frequency returns into a practical loss threshold for a chosen confidence level. With 99% confidence, the model estimates a loss that should be exceeded about one time in one hundred comparable horizons. Traders often monitor this metric alongside position limits to prevent drift during fast markets and to support consistent escalation rules. Many desks also log VaR by strategy, updating it every 5–15 minutes for supervision and aligns with mandated intraday reporting cycles.
VaR rises as the confidence level increases because you are looking deeper into the loss tail. The horizon also matters: scaling from 5 minutes to 30 minutes uses the square‑root factor √(30/5)=2.449 under independence assumptions. If returns show clustering, this scaling can understate risk, so compare multiple horizons and validate against realized drawdowns. When liquidity is thin, use shorter intervals to capture gap risk more accurately.
Historical simulation uses the empirical distribution of scaled returns and is transparent when the dataset is clean. Parametric VaR assumes normality, summarizing the distribution with mean and standard deviation. If the return series is skewed or heavy‑tailed, historical estimates typically produce larger tail losses than the normal model, especially during event windows. A quick check is kurtosis or large outlier frequency in the sample.
EWMA volatility reacts faster to regime shifts by weighting recent returns more heavily. A lambda near 0.94 emphasizes the latest observations, while 0.97 smooths noise for intraday streams. In practice, EWMA can raise VaR shortly after a shock, which helps reduce exposure before losses compound. Use it when volatility persistence is evident. Pair it with alerts when σ jumps beyond a rolling percentile threshold.
Use a rolling window and count exceptions: if you target 99% confidence, the expected exception rate is 1%. For 390 minutes of trading, a daily backtest over 250 days implies about 2 to 3 exceptions if the model is well calibrated. Track both VaR and Expected Shortfall, because ES penalizes the severity of tail outcomes. Combine these metrics with stop‑loss bands and intraday stress scenarios for governance.
Paste returns as percentages for your chosen interval, separated by commas or new lines. For example, -0.05 means a 0.05% loss over that interval.
More is better for tail stability. Aim for several hundred intraday intervals when possible, and refresh the window as market conditions change.
Normal VaR compresses returns into mean and volatility. Historical VaR reflects the observed tail, which can be heavier, skewed, and impacted by jumps.
Use EWMA when volatility clusters and recent moves matter more than older ones. It adapts quickly after shocks and may tighten limits sooner.
No. Square‑root scaling assumes independent increments and stable variance. If microstructure noise or clustering dominates, validate using direct horizon returns or a volatility model.
Expected Shortfall estimates the average loss beyond VaR. It is more sensitive to extreme outcomes and complements VaR for stress and limit setting.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.