Standard deviation and variance of a portfolio – finance train examples of money laundering cases

We learned about how to calculate the standard deviation of a single asset. Let’s now look at how to calculate the standard deviation of a portfolio with two or more assets.

The returns of the portfolio were simply the weighted average of returns of all assets in the portfolio. However, the calculation of the risk/standard deviation is not the same. While calculating the variance, we also need to consider the covariance between the assets in the portfolio. If the assets are perfectly correlated, then the simple weighted average of variances will work. However, when we have to account for the covariance, the equation will change.

Covariance reflects the degree to which two securities vary or change together, and is represented as cov (ri,rj). The problem with covariance is that it has no units, and is difficult to compare across assets.

Using covariance, we can calculate the correlation between the assets using the following formula: correlation

examples of money laundering cases

After incorporating covariance, the standard deviation of a two-asset portfolio can be calculated as follows: standard deviation of a two asset portfolio

In general as the correlation reduces, the risk of the portfolio reduces due to the diversification benefits. Two assets a perfectly negatively correlated provide the maximum diversification benefit and hence minimize the risk.

Let’s take an example to understand the calculation. Assume we have a portfolio with the following details: example

The standard deviation can be calculated as follows:

This portfolio has an expected return of 16.80% and portfolio risk of 11.09%.

We learned about how to calculate the standard deviation of a single asset. Let’s now look at how to calculate the standard deviation of a portfolio with two or more assets.

The returns of the portfolio were simply the weighted average of returns of all assets in the portfolio.Examples of money laundering cases however, the calculation of the risk/standard deviation is not the same. While calculating the variance, we also need to consider the covariance between the assets in the portfolio. If the assets are perfectly correlated, then the simple weighted average of variances will work. However, when we have to account for the covariance, the equation will change.

Covariance reflects the degree to which two securities vary or change together, and is represented as cov (ri,rj). The problem with covariance is that it has no units, and is difficult to compare across assets. Using covariance, we can calculate the correlation between the assets using the following formula: correlation

After incorporating covariance, the standard deviation of a two-asset portfolio can be calculated as follows: standard deviation of a two asset portfolio

In general as the correlation reduces, the risk of the portfolio reduces due to the diversification benefits.Examples of money laundering cases two assets a perfectly negatively correlated provide the maximum diversification benefit and hence minimize the risk.

Let’s take an example to understand the calculation. Assume we have a portfolio with the following details: example

The standard deviation can be calculated as follows:

This portfolio has an expected return of 16.80% and portfolio risk of 11.09%.

We learned about how to calculate the standard deviation of a single asset. Let’s now look at how to calculate the standard deviation of a portfolio with two or more assets.

The returns of the portfolio were simply the weighted average of returns of all assets in the portfolio. However, the calculation of the risk/standard deviation is not the same. While calculating the variance, we also need to consider the covariance between the assets in the portfolio. If the assets are perfectly correlated, then the simple weighted average of variances will work.Examples of money laundering cases however, when we have to account for the covariance, the equation will change.

Covariance reflects the degree to which two securities vary or change together, and is represented as cov (ri,rj). The problem with covariance is that it has no units, and is difficult to compare across assets. Using covariance, we can calculate the correlation between the assets using the following formula: correlation

After incorporating covariance, the standard deviation of a two-asset portfolio can be calculated as follows: standard deviation of a two asset portfolio

In general as the correlation reduces, the risk of the portfolio reduces due to the diversification benefits. Two assets a perfectly negatively correlated provide the maximum diversification benefit and hence minimize the risk.

Let’s take an example to understand the calculation. Assume we have a portfolio with the following details: example

examples of money laundering cases

The standard deviation can be calculated as follows:

This portfolio has an expected return of 16.80% and portfolio risk of 11.09%.