Understanding Portfolio Variance
Portfolio variance is a measure of the risk or how the actual returns of a portfolio’s constituent securities fluctuate over time. This critical statistic uses the standard deviations of each individual security as well as the correlations between different security pairs within the portfolio.
Key Aspects of Portfolio Variance
- Measuring Overall Risk: Portfolio variance equates to the standard deviation squared, reflecting the portfolio’s total risk exposure.
- Incorporating Individual Metrics: This measure integrates individual asset weights, variances, and their co-variances.
- Impact of Correlation: Lower correlation among securities leads to reduced portfolio variance.
- Role in Modern Portfolio Theory (MPT): Portfolio variance, together with standard deviation, defines the risk axis on the efficient frontier.
Calculating Portfolio Variance
Portfolio variance accounts for the covariance or correlation coefficients of assets within the portfolio. A lower correlation generally results in reduced variance. To compute this metric, multiply the squared weight of each asset by its variance, then add double the weighted mean weight multiplied by each security pair’s covariance.
Essential Formula for Two-Asset Portfolio:
Portfolio variance = w1^2σ1^2 + w2^2σ2^2 + 2w1w2Cov1,2
Where:
w1= weight of the first assetw2= weight of the second assetσ1= standard deviation of the first assetσ2= standard deviation of the second assetCov1,2= covariance between the two assets, which equalsp(1,2)σ1σ2, wherep(1,2)is the correlation coefficient between them
As asset count increases, variance calculation terms multiply exponentially, making software tools invaluable for computation.
Portfolio Variance and Modern Portfolio Theory
Modern Portfolio Theory (MPT) constructs portfolios with an aim of optimizing returns while minimizing risk, conceptually by seeking an efficient frontier—the least risk for the desired return. Reducing risk in portfolios is usually achieved by investing in non-correlated assets. Thus, assets considered risky individually might reduce overall portfolio risk through diversification.
Importance of Standard Deviation in MPT
Risk assessment in portfolios often uses standard deviation (square root of variance) to quantify overall portfolio risk. A high standard deviation implies high volatility. Financial advisors and portfolio managers routinely report and analyze standard deviation.
A Practical Example of Portfolio Variance
Imagine a portfolio with two stocks. Stock A is valued at $50,000 with a 20% standard deviation, and Stock B is worth $100,000 with a 10% standard deviation. Their correlation is 0.85. Weights are subsequently 33.3% for Stock A and 66.7% for Stock B. Using the formula, variance computes to:
Variance = (33.3%² × 20%²) + (66.7%² × 10%²) + (2 × 33.3% × 20% × 66.7% × 10% × 0.85) = 1.64%
Interpreting variance alone can be complex, hence analysts often favor standard deviation, or the square root of variance. Here it rounds to 12.81%.
Wrap-Up: Leveraging Portfolio Variance
Initially challenging to interpret, portfolio variance is central in quantifying portfolio risk. The real significance lies in analyzing standard deviation, providing clarity on potential volatilities. Portfolio managers frequently adjust holdings, incorporating lower correlation assets, to moderate risk and thus lower portfolio standard deviation.
Understanding and leveraging portfolio variance fundamentally enables more robust investment strategy and more accurate risk management.
Related Terms: standard deviation, correlation coefficients, covariance, asset classes, efficient frontier.
