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calculating the volatility for a single stock is straightforward. However, I'm not sure whether my approach for calculating the volatility matrix for multiple stocks is correct:

I assume a log-normal distribution of the stock prices. Therefore I calculate the log returns, calculate the variance-covariance matrix of the log returns and perform the cholesky decomposition of the variance-covariance matrix. The result from the last step is, as far as I am aware of, the volatility matrix.

Let ln_sec be a m x n matrix with the log returns of n securities over a period of m months, then my matlab code is as follows:

vcm = cov(ln_sec) %var-cov matrix
vcm = vcm * 12 %annualize var-cov matrix
vm = chol(vcm) %volatility matrix

Would you agree on my approach?

Thank you.

Edit: I forgot to mention the application in order to discuss a suitable solution. The volatility matrix is used to calculate a market price of risk vector as follows

(drift - risk-free-rate*1)/vm

where drift is a n x 1 vector with the mean drift rates of n assets and 1 is a n x 1 vector with 1's. The market price of risk vector is then used to simulate a state-price density (pricing kernel).

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1 Answer 1

You can just take the diagonal of the var-cov matrix. This should give you the variance of each stock and then take sqrt of that for std. deviation.

sd = sqrt(diag(vcm))
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thanks for your answer. I'm struggling with your solution because I would disregard the correlation between the assets... –  Clems Aug 9 at 9:27
    
Then it depends completely on the application of why are you doing this in the first place. I assumed you are looking at stocks individually. Can you elaborate what will you be using this for? Sometimes application derives what approach to use. –  Taran Aug 9 at 9:35
    
thanks for the hint. I edited my question above. –  Clems Aug 9 at 9:51

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