Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master’s in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.
Somer G. Anderson is CPA, doctor of accounting, and an accounting and finance professor who has been working in the accounting and finance industries for more than 20 years. Her expertise covers a wide range of accounting, corporate finance, taxes, lending, and personal finance areas.
Hans Daniel Jasperson has over a decade of experience in public policy research, with an emphasis on workforce development, education, and economic justice. His research has been shared with members of the U.S. Congress, federal agencies, and policymakers in several states.
What Is Portfolio Variance?
Portfolio variance is a measurement of risk, of how the aggregate actual returns of a set of securities making up a portfolio fluctuate over time. This portfolio variance statistic is calculated using the standard deviations of each security in the portfolio as well as the correlations of each security pair in the portfolio.
- Portfolio variance is a measure of a portfolio’s overall risk and is the portfolio’s standard deviation squared.
- Portfolio variance takes into account the weights and variances of each asset in a portfolio as well as their covariances.
- A lower correlation between securities in a portfolio results in a lower portfolio variance.
- Portfolio variance (and standard deviation) define the risk-axis of the efficient frontier in modern portfolio theory (MPT).
Understanding Portfolio Variance
Portfolio variance looks at the covariance or correlation coefficients for the securities in a portfolio http://4crypto.info. Generally, a lower correlation between securities in a portfolio results in a lower portfolio variance.
Portfolio variance is calculated by multiplying the squared weight of each security by its corresponding variance and adding twice the weighted average weight multiplied by the covariance of all individual security pairs.
Modern portfolio theory says that portfolio variance can be reduced by choosing asset classes with a low or negative correlation, such as stocks and bonds, where the variance (or standard deviation) of the portfolio is the x-axis of the efficient frontier.
Formula and Calculation of Portfolio Variance
The most important quality of portfolio variance is that its value is a weighted combination of the individual variances of each of the assets adjusted by their covariances. This means that the overall portfolio variance is lower than a simple weighted average of the individual variances of the stocks in the portfolio.
The formula for portfolio variance in a two-asset portfolio is as follows:
- w1 = the portfolio weight of the first asset
- w2 = the portfolio weight of the second asset
- σ1= the standard deviation of the first asset
- σ2 = the standard deviation of the second asset
- Cov1,2 = the covariance of the two assets, which can thus be expressed as p(1,2)σ1σ2, where p(1,2) is the correlation coefficient between the two assets
The portfolio variance is equivalent to the portfolio standard deviation squared.
As the number of assets in the portfolio grows, the terms in the formula for variance increase exponentially. For example, a three-asset portfolio has six terms in the variance calculation, while a five-asset portfolio has 15.
Portfolio Variance and Modern Portfolio Theory
Modern portfolio theory (MPT) is a framework for constructing an investment portfolio. MPT takes as its central premise the idea that rational investors want to maximize returns while also minimizing risk, sometimes measured using volatility. Investors seek what is called an efficient frontier, or the lowest level of risk and volatility at which a target return can be achieved.
Risk is lowered in MPT portfolios by investing in non-correlated assets. Assets that might be risky on their own can actually lower the overall risk of a portfolio by introducing an investment that will rise when other investments fall. This reduced correlation can reduce the variance of a theoretical portfolio.
In this sense, an individual investment’s return is less important than its overall contribution to the portfolio, in terms of risk, return, and diversification.
The level of risk in a portfolio is often measured using standard deviation, which is calculated as the square root of the variance. If data points are far away from the mean, the variance is high, and the overall level of risk in the portfolio is high as well. Standard deviation is a key measure of risk used by portfolio managers, financial advisors, and institutional investors. Asset managers routinely include standard deviation in their performance reports.
Example of Portfolio Variance
For example, assume there is a portfolio that consists of two stocks. Stock A is worth $50,000 and has a standard deviation of 20%. Stock B is worth $100,000 and has a standard deviation of 10%. The correlation between the two stocks is 0.85. Given this, the portfolio weight of Stock A is 33.3% and 66.7% for Stock B. Plugging in this information into the formula, the variance is calculated to be:
- Variance = (33.3%^2 x 20%^2) + (66.7%^2 x 10%^2) + (2 x 33.3% x 20% x 66.7% x 10% x 0.85) = 1.64%
Variance is not a particularly easy statistic to interpret on its own, so most analysts calculate the standard deviation, which is simply the square root of variance. In this example, the square root of 1.64% is 12.81%.