Analyze paired asset movements with structured data inputs. Switch sample settings, precision, and annualization easily. Turn return pairs into useful portfolio risk insight fast.
This example uses percentage returns. The sample covariance result is 0.000490 when converted into decimals.
| Period | Asset A Return (%) | Asset B Return (%) |
|---|---|---|
| 1 | 4 | 3 |
| 2 | 2 | 1 |
| 3 | -1 | 0 |
| 4 | 3 | 4 |
| 5 | 5 | 6 |
Sample covariance: Cov(A,B) = Σ[(Ai - MeanA) × (Bi - MeanB)] / (n - 1)
Population covariance: Cov(A,B) = Σ[(Ai - MeanA) × (Bi - MeanB)] / n
Annualized covariance: Annualized Covariance = Covariance × Annualization Factor
This calculator also reports mean returns, variance, standard deviation, correlation, and the sum of cross deviations.
A covariance of two assets calculator helps measure how two return series move together. It is a practical tool for portfolio analysis, diversification studies, and risk review. Positive covariance suggests both assets often move in the same direction. Negative covariance suggests they tend to move opposite each other. A value near zero suggests weaker linear co-movement.
This page is useful for data science workflows because it turns raw paired observations into clean risk metrics. You can paste decimal returns or percentage returns. You can switch between sample covariance and population covariance. You can also apply an annualization factor when your source data is daily, weekly, or monthly.
The output does more than show one covariance number. It also reports mean return for each asset, variance for each series, standard deviation, correlation, and the sum of cross deviations. These extra values help you validate the data and understand why the final covariance changed. This is helpful when comparing asset pairs, factor baskets, sector ETFs, or strategy returns.
Covariance alone is not a complete risk signal. It depends on scale. That is why the calculator also shows correlation. Correlation standardizes the relationship and makes comparison easier across different assets. Together, covariance and correlation give a stronger view of co-movement inside a portfolio.
Use sample covariance when your observations are a subset of a larger process. This is common in backtesting, rolling windows, and historical portfolio analysis. Use population covariance when the full data set represents the entire group you want to measure. The choice changes the denominator and slightly changes the result.
Always keep both asset series aligned by time. Missing or mismatched observations can distort the output. With clean paired inputs, this covariance of two assets calculator becomes a fast way to inspect diversification strength, risk clustering, and shared return behavior.
Covariance shows whether two assets tend to move together. A positive value suggests similar direction. A negative value suggests opposite movement. A value near zero suggests weak linear co-movement across the paired observations.
Covariance measures joint movement in original scale. Correlation rescales that relationship to a standard range between -1 and 1. Correlation is easier for comparison across different assets, while covariance is useful in portfolio math.
Use sample covariance when your return series is only a sample from a larger process. This is common with historical windows, backtests, and recent market observations. It uses n minus 1 in the denominator.
Use population covariance when the data set represents the full group you want to evaluate. It uses n in the denominator. This is less common in market sampling but useful in complete internal data sets.
Yes. Choose percentage input mode, then enter values like 4, 2, and -1. The calculator converts them into decimals internally before computing covariance, variance, standard deviation, and correlation.
Covariance compares paired observations from the same periods. Each value in one series needs a matching value in the other series. Unequal lengths break that pairing and make the calculation invalid.
Annualized covariance scales the base covariance by the factor you enter. For example, daily return covariance can be multiplied by 252. This helps align the result with annual portfolio reporting or risk models.
Yes. Covariance helps you inspect how strongly two assets move together. Lower or negative covariance can support diversification. It is especially useful when reviewing portfolio construction, hedging logic, or factor exposure overlap.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.