Expected Return and Variance for a Two Asset Portfolio

Expected Return for a Two Asset Portfolio

The expected return of a portfolio is equal to the weighted average of the returns on individual assets in the portfolio.

Rp = w1R1 + w2R2

  • Rp = expected return for the portfolio
  • w1 = proportion of the portfolio invested in asset 1
  • R1 = expected return of asset 1

Expected Variance for a Two Asset Portfolio

The variance of the portfolio is calculated as follows:

σp2 = w12σ12 + w22σ22 + 2w1w2Cov1,2

  • Cov1,2 = covariance between assets 1 and 2
  • Cov1,2 \= ρ1,2 * σ1 * σ2; where ρ = correlation between assets 1 and 2

The above equation can be rewritten as:

σp2 = w12σ12 + w22σ22 + 2w1w2 ρ1,2σ1σ2

Keep in mind that this is the calculation for portfolio variance. If a test question asks for the standard deviation then you will need to take the square root of the variance calculation. Percentage values can be used in this formula for the variances, instead of decimals.

Example 

The following information about a two stock portfolio is available:

 Stock AStock B
Amount20,00030,000
Expected Returns12%20%
Standard Deviation20%30%
Correlation

0.25

|

The weights for the two assets are:

wA\= 20,000/50,000 = 40%

wB\= 30,000/50,000 = 60%

Expected Returns = 0.40*0.12 + 0.60*0.20 = 16.8%

Variance = (0.40)2(0.20) 2 + (0.60) 2 (0.30) 2 + 2(0.40)(0.60)(0.25)(0.20)(0.30)

\= 0.046

Standard deviation = Sqrt(0.046) = 0.2145 or 21.45%

Expected Variance for a Three Asset Portfolio

σp2 = w12σ12 + w22σ22 + w32σ32 + 2w1w2Cov1,2 + 2w1w3Cov1,3 + 2w2w3Cov2,3

Membership
Learn the skills required to excel in data science and data analytics covering R, Python, machine learning, and AI.
I WANT TO JOIN
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Saylient AI Logo

Take the Next Step in Your Data Career

Join our membership for lifetime unlimited access to all our data analytics and data science learning content and resources.