Portfolio Risk Analytics

Last reviewed: by .

Professional-grade risk analytics including Value at Risk, stress testing, and scenario analysis to understand and manage portfolio risk exposure. Available with a Professional subscription.

VaR vs Stress vs Tail Risk: When Each One Matters

The three risk measures answer different questions. VaR answers "in a normal-ish day, how bad could the typical worst case be?" A 95% VaR of $4,000 means that, on roughly 1 day in 20, the portfolio would lose at least that much under historical or parametric assumptions. It is calibrated to recent regime data, so it is most useful when conditions resemble that regime. Stress tests answer "what if conditions do not resemble that regime?" They let you bolt on a 2008-style equity drop, a 2020-style vol spike, or a custom rate-hike scenario and see how the book responds. Tail risk and expected shortfall answers "given that I am in the worst 5% of outcomes, what is the average loss?" This is important because two portfolios can have identical VaR but very different expected losses in the tail.

Workflow: Sizing a New Position

Before adding a position, the typical pattern is: open the Risk page with the existing book loaded, add a hypothetical leg, and watch how VaR and expected shortfall change. If the marginal contribution to risk is small, the position is well-diversified relative to the rest of the book; if it is large, you are concentrating risk in whatever factor that position carries (vol, rates, single-name beta). The correlation matrix helps explain why: if the new position has high correlation with several existing ones, the marginal risk contribution will be larger than the position's standalone risk would suggest.

What This Page Is Not

Risk metrics are model-based estimates, not guarantees. VaR misses regime breaks (the exact moment risk is highest, the historical window is least informative); stress scenarios are only as good as the shocks you choose; the efficient frontier assumes returns and covariances are stationary, which they are not over the timescales most retail trades operate on. Treat these tools as a discipline for surfacing concentrations and asymmetries, not as a "the model says I'm safe" green light.

Choosing VaR Type: Parametric vs Historical

The page supports both parametric VaR (Gaussian distributional assumption) and historical VaR (empirical percentile of actual return history). Parametric VaR is fast to compute and stable across small sample sizes but understates tail risk because returns are usually fatter-tailed than Gaussian. Historical VaR is the more honest estimator when there is sufficient history, but it can be unstable on books that include positions with limited history (recent IPOs, newly listed options series). The typical pattern is to read both: a wide parametric vs historical gap is itself informative about how non-Gaussian the book's exposures are. The 95% confidence level is more practically useful than 99% because the 99% number is dominated by data quality at the extreme tail.

Reading the Correlation Matrix

The correlation matrix is the entry point for diversification decisions. High positive correlation between two positions (above 0.7) means they will move together in most market conditions, so combining them does not reduce risk meaningfully; the book's effective concentration is higher than the position count suggests. Low or negative correlation (below 0.3, or negative) is what diversification actually looks like. Correlations are computed on returns over the selected lookback window; they are not stable over time, so a position pair that looked diversifying in one regime can co-move heavily in another (the 2020 March cross-asset correlation spike is the canonical example). Read the matrix as a current-state diagnostic, not a permanent property of the book.

Stress Scenario Design

The stress scenario builder accepts equity shocks (percentage spot move), volatility shocks (IV multiplicative or additive), rate shocks (parallel curve shift), and time shocks (theta over a horizon). The pre-built scenarios cover the common cases: 2008-style equity crash with vol spike, 2018 February vol-of-vol spike, 2020 March crisis with cross-asset correlation jump, 2022 rate-hike year with sustained vol elevation. The scenario builder runs each shock through the position-level Greeks model so the output is a P-and-L estimate per position and an aggregated portfolio P-and-L. The estimates are linear in the shocks (using the current Greeks); they will become inaccurate at large shocks where second-order effects dominate, which is why custom Monte Carlo through the Backtesting page is the right tool for very large hypothetical moves.

Margin and Buying Power Implications

The margin analysis panel estimates the margin requirement and buying power impact of the current book under standard broker-margin rules (Reg T for equity, portfolio margin where applicable, options-specific rules for spreads and short-premium positions). The estimates are approximations because actual margin varies by broker and by the broker's portfolio margin model; the panel uses conservative defaults so the displayed requirement is usually slightly higher than what the broker will actually charge. The most useful read is whether a contemplated new position will push the book past 50% buying power utilization (a common discipline threshold) before checking with the broker.

This page is part of the Options Analysis Suite features overview. Browse the full documentation.