The Variance-Covariance approach is also known as the Delta-Normal approach. It assumes that the returns of the assets in the portfolio follow a normal distribution, which allows for a relatively simple calculation.
Here are the steps to calculate VaR using the Variance-Covariance approach:
1. Data Collection: Gather historical price or return data for all the assets in the portfolio over the chosen time horizon.
2. Portfolio Composition: Determine the composition of your portfolio by specifying the weights of each asset. The portfolio can consist of various assets like stocks, bonds, or other financial instruments.
3. Calculate Portfolio Returns: Calculate the returns of your portfolio using the collected historical data. Portfolio returns are typically a weighted sum of individual asset returns. The formula for portfolio return is:
Portfolio Return = Σ (Weight of Asset i * Return of Asset i)
4. Calculate Portfolio Mean Return: Compute the mean return of the portfolio. It represents the expected return of the portfolio over the chosen time horizon.
5. Calculate Portfolio Standard Deviation: Determine the standard deviation of the portfolio returns. The standard deviation measures the dispersion of returns around the mean return and reflects the portfolio’s overall risk.
6. Determine Confidence Level: Choose the desired confidence level for VaR calculation. Common confidence levels used are 95% and 99%, representing the level of certainty desired.
7. Calculate the Z-Score: The Z-score represents the number of standard deviations away from the mean return at the chosen confidence level. For a normal distribution, the Z-score corresponding to a specific confidence level can be obtained from the standard normal distribution table.
8. Calculate VaR: Finally, calculate the VaR by multiplying the Z-score with the portfolio’s standard deviation. The formula for VaR using the Variance-Covariance approach is:
VaR = Portfolio Standard Deviation * Z-Score
In conclusion, the Variance-Covariance approach is a simple and computationally efficient method for estimating VaR. However, it’s essential to be aware of its assumptions, particularly the normality assumption.
