Portfolio Variance Calculator
This calculator computes portfolio variance and standard deviation using three workflows: a closed-form two-asset formula, a general covariance matrix quadratic form, and estimation from historical returns. It supports supplying either a covariance matrix or deriving one from standard deviations and a correlation matrix.
The tool focuses on practical reliability: it validates basic input shapes, clearly documents assumptions (sample vs population estimators, annualization factors), and surfaces accuracy caveats so you can interpret results appropriately.
Governance
Record f918017cd68b • Reviewed by Fidamen Standards Committee
Compute portfolio variance using an input covariance matrix or derive covariance from standard deviations and a correlation matrix. Uses the quadratic form w' Σ w.
Inputs
Advanced inputs
Two-asset inputs
Matrix inputs (N assets)
Historical returns inputs
Results
Portfolio variance (decimal)
—
Portfolio standard deviation (annualized, %)
—
| Output | Value | Unit |
|---|---|---|
| Portfolio variance (decimal) | — | — |
| Portfolio standard deviation (annualized, %) | — | — |
Visualization
Methodology
Two-asset formula: variance is computed using the direct algebraic formula for two assets: var = w1^2 σ1^2 + w2^2 σ2^2 + 2 w1 w2 ρ12 σ1 σ2.
General method: for N assets the portfolio variance is the quadratic form w' Σ w, where w is the weights column vector and Σ is the covariance matrix. If a covariance matrix is not provided, Σ may be constructed from standard deviations and a correlation matrix by Σ_ij = ρ_ij σ_i σ_j.
Estimation from returns: when historical return series are provided the sample or population covariance matrix is estimated from the returns table. The estimator choice (sample vs population) and the annualization factor materially affect the magnitude of variance and must match how the returns were computed.
Worked examples
Two-asset example: w1=0.6, w2=0.4, σ1=10% (0.10), σ2=20% (0.20), ρ=0.25 → compute using the two-asset formula to get portfolio variance and standard deviation.
Matrix example: paste weights 0.2,0.3,0.5 and provide a 3x3 covariance matrix (rows separated by semicolon). The tool computes w' Σ w directly.
Estimation example: paste daily returns for three assets (rows = days). Select the sample estimator and set annualization_period=252 to obtain annualized variance and standard deviation.
F.A.Q.
Should I provide a covariance matrix or a correlation matrix with standard deviations?
Providing a covariance matrix avoids reconstruction errors and is preferred when available. If you only have correlations, the tool will reconstruct Σ using Σ_ij = ρ_ij σ_i σ_j; ensure standard deviations and correlations correspond to the same return frequency and measurement convention.
How does annualization work and when should I use it?
Variance scales linearly with time units. To annualize period variance multiply by the number of periods per year (e.g., 252 for daily). Standard deviation annualized is the square root of the annualized variance. Use annualization only when returns and covariances are computed on a consistent periodic basis.
What estimator should I use: sample or population?
Use the sample estimator if you are estimating from observed data and want an unbiased estimator of population covariance for most inference tasks (divide by N-1). The population estimator (divide by N) is appropriate when you treat the supplied data as the full population of interest.
What input validation does the tool perform?
The tool checks vector/matrix dimension compatibility (weights length matches number of assets), enforces weights sum warnings (does not require sum-to-one but will warn), and detects malformed CSV inputs. It does not replace human review for data quality or outlier handling.
What are common failure modes or limits?
Covariance matrices must be positive semidefinite for valid variances; estimated matrices from small samples may be ill-conditioned. Annualization assumptions, mismatched frequencies, and inconsistent return definitions (log vs arithmetic) are common sources of error.
Sources & citations
- NIST Digital Library of Mathematical Functions (general numerical methods guidance) — https://www.nist.gov/
- ISO: Risk management and related standards (context for model risk controls) — https://www.iso.org/
- IEEE: Standards for numerical software and recommended practices — https://www.ieee.org/
- OSHA: Operational risk & safety best practices for workplaces (context for governance) — https://www.osha.gov/
- SEC — Investor.gov Educational Resources — https://www.investor.gov/
- CFA Institute — Global Investment Performance Standards (GIPS) — https://rpc.cfainstitute.org/gips-standards
- FINRA — Investment Products — https://www.finra.org/investors/investing/investment-products
Further resources
Versioning & Change Control
Audit record (versions, QA runs, reviewer sign-off, and evidence).
Record ID: f918017cd68bWhat changed (latest)
v1.0.0 • 2025-11-09 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Public QA status
PASS — golden 25 + edge 120
Last run: 2026-01-23 • Run: golden-edge-2026-01-23
Versioning & Change Control
Audit record (versions, QA runs, reviewer sign-off, and evidence).
What changed (latest)
v1.0.0 • 2025-11-09 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Public QA status
PASS — golden 25 + edge 120
Last run: 2026-01-23 • Run: golden-edge-2026-01-23
Engine
v1.0.0
Data
Baseline (no external datasets)
Content
v1.0.0
UI
v1.0.0
Governance
Last updated: Nov 9, 2025
Reviewed by: Fidamen Standards Committee (Review board)
Credentials: Internal QA
Risk level: low
Reviewer profile (entity)
Fidamen Standards Committee
Review board
Internal QA
Entity ID: https://fidamen.com/reviewers/fidamen-standards-committee#person
Semantic versioning
- MAJOR: Calculation outputs can change for the same inputs (formula, rounding policy, assumptions).
- MINOR: New features or fields that do not change existing outputs for the same inputs.
- PATCH: Bug fixes, copy edits, or accessibility changes that do not change intended outputs except for previously incorrect cases.
Review protocol
- Verify formulas and unit definitions against primary standards or datasets.
- Run golden-case regression suite and edge-case suite.
- Record reviewer sign-off with credentials and scope.
- Document assumptions, limitations, and jurisdiction applicability.
Assumptions & limitations
- Uses exact unit definitions from the Fidamen conversion library.
- Internal calculations use double precision; display rounding follows the unit's configured decimal places.
- Not a substitute for calibrated instruments in regulated contexts.
- Jurisdiction-specific rules may require official guidance.
Change log
v1.0.0 • 2025-11-09 • MINOR
Initial publication and governance baseline.
Why: Published with reviewed formulas, unit definitions, and UX controls.
Areas: engine, content, ui • Reviewer: Fidamen Standards Committee • Entry ID: 8a784c885e99
