Hey,
I have a portfolio of 20 stocks over a time period of 15 years. I calculated the mean returns and the covariance matrix. Now I want to perform a Monte Carlo simulation based on a multivariate normal distribution with the same parameters. I downloaded the “real statistics resource pack” for excel to actually generate these randomly generated returns for the 20 stocks over the time period. Based on the newly generated returns I again calculate the mean returns and the covariance matrix.
The problem is:
The covariance matrix based on the random sample has a lot of minus values compared to the original covariance matrix where there is not one minus value. Moreover, the new covariance matrix is way smaller than the original covariance matrix.
Is that normal? Because my efficient frontier optimization would not plot almost in the same area of the original sample. The standard deviation is too small.
I have a portfolio of 20 stocks over a time period of 15 years. I calculated the mean returns and the covariance matrix. Now I want to perform a Monte Carlo simulation based on a multivariate normal distribution with the same parameters. I downloaded the “real statistics resource pack” for excel to actually generate these randomly generated returns for the 20 stocks over the time period. Based on the newly generated returns I again calculate the mean returns and the covariance matrix.
The problem is:
The covariance matrix based on the random sample has a lot of minus values compared to the original covariance matrix where there is not one minus value. Moreover, the new covariance matrix is way smaller than the original covariance matrix.
Is that normal? Because my efficient frontier optimization would not plot almost in the same area of the original sample. The standard deviation is too small.