Two-Asset Portfolio Risk Decomposition

When you combine two risky assets into a portfolio, the portfolio's expected return is simply the weighted average of the individual expected returns. But portfolio risk is not a weighted average — it depends critically on how the two assets move together. With weights \(w_A\) and \(w_B = 1 - w_A\), the portfolio variance is

$$\sigma_p^2 \;=\; w_A^2\,\sigma_A^2 \;+\; w_B^2\,\sigma_B^2 \;+\; 2\,w_A\,w_B\,\rho_{AB}\,\sigma_A\,\sigma_B$$

The third term, \(2\,w_A\,w_B\,\rho_{AB}\,\sigma_A\,\sigma_B\), is the covariance contribution. When \(\rho_{AB} < 1\) it is smaller than it would be under perfect co-movement, so total portfolio risk falls below what a simple weighted average would suggest. When \(\rho_{AB} \leq 0\) the term is zero or negative and actively reduces portfolio variance — the essence of diversification.

The visualization

The chart plots the portfolio's expected return against its standard deviation as the weight in Asset A varies from 0% to 100%. The highlighted point shows your currently selected portfolio. Adjusting the correlation slider changes the shape of the curve; adjusting the weight slider moves the highlighted point along it.

Asset A

Expected return 10.0%
Standard deviation 20.0%

Asset B

Expected return 5.0%
Standard deviation 10.0%

Portfolio

Correlation (ρAB) 0.30
Weight in Asset A (wA) 50%

Portfolio Results

Weight in A / B 50% / 50%
Expected return 7.5%
Portfolio variance
Portfolio std dev
Wtd avg σ (if ρ = 1)
Diversification benefit
Asset A
Asset B
Portfolio frontier
Selected portfolio

Portfolio Variance Decomposition

Asset A component: wA²σA²
Asset B component: wB²σB²
Covariance term: 2wAwBρσAσB
Return: E[Rp] = wA·E[RA] + wB·E[RB] (weighted average)
Variance: σp² = wA²σA² + wB²σB² + 2wAwBρABσAσB (not a weighted average)
Covariance: Cov(A, B) = ρAB·σA·σB

Key takeaways

  1. Expected return is a weighted average; risk is not. Combining assets with weights 60/40 gives a 60/40 weighted average return, but the portfolio standard deviation is generally less than the weighted average of the individual standard deviations.
  2. Correlation drives diversification. The lower the correlation, the more the frontier bows to the left, and the more risk is reduced at any given expected return.
  3. Negative correlation is the most powerful diversifier. When ρ < 0 the covariance term subtracts from variance, potentially reducing portfolio risk below either individual asset.
  4. The minimum-variance portfolio is not 0%/100% or 100%/0%. There is an interior weight that minimises risk, and that weight depends on both standard deviations and the correlation.
  5. Covariance, not variance, is added at the margin. When you add an asset to a portfolio, the incremental contribution to risk is determined by how it co-moves with the existing portfolio, not by its own variance.

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