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Covariance Portfolio Risk Contribution Calculator

A portfolio's largest holding is not always its largest contributor to volatility. Contribution depends on both the holding's own variation and how it moves with the rest of the portfolio. This calculator builds a sample covariance matrix from three aligned return series, applies fixed long-only weights, and decomposes the resulting volatility. It is useful for inspecting diversification in a hypothetical crypto portfolio without assuming that equal capital weights imply equal risk or that historical relationships will remain unchanged.

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Allocate volatility through covariance

Convert percentage weights to fractions w that sum to one, and calculate the sample covariance matrix C using N − 1. Portfolio variance is w′Cw and periodic standard deviation is σp = sqrt(w′Cw). Asset i contributes RCᵢ = wᵢ(Cw)ᵢ/σp. Contributions sum to portfolio standard deviation when it is positive. Annualize both total volatility and each contribution by sqrt(P), where P is observations per year. The contribution unit is percentage points of portfolio volatility.

A positive holding can reduce total variation

Consider three aligned return series: A is 1%, −1%, 0%; B is 2%, −2%, 0%; and C is −1%, 1%, 0%. With weights 50%, 25%, and 25%, portfolio returns are 0.75%, −0.75%, and 0%. Sample portfolio volatility is 0.75% per observation period. The signed contributions are 0.50, 0.50, and −0.25 percentage points, which sum to 0.75. Asset C has a positive capital weight yet offsets variation in this sample.

Use the signs without overstating diversification

A negative contribution describes a covariance relationship in the entered history; it does not mean that the asset cannot lose money. Return dates, currency units, and sampling frequency must align across all three series. A zero-volatility portfolio has no finite relative contribution decomposition. Fixed weights represent a constant-weight calculation rather than a drifting buy-and-hold account, and rebalancing costs are not included. A short sample can produce unstable covariances, so compare multiple windows before using contributions in a portfolio decision.

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