Which kind of normalization to prefer before PCA (generic solution for any factor analysis)

I have financial assets with totally different volatilities, thus I must standardize them before PCA, otherwise, assets with high variance may be considered as principle components, which is wrong.

At the moment I am trying to decide among following methods :

• Calculate all in USD, divide each one by volatility or deviation coefficient, substract mean


• Get logarithm on each price, substract mean


• Limit time series by [-1:1]

Image below describes how time series look like after transformation, coefficients at the right is the first eigenvector.

Question : as you can see on the image, coefficients for each method are quite different and I would like to get an advice about which standardization method looks more appropriate in this case and does not create biases in calculations?

Purpose : I do not need unit form vectors, thus, I calculate PCA based on covariance matrix and want to have vectors that really represent projection of specific asset to selected principal component.

I think, that if second window shows lowest variance it means that usage of logarithms is the best option, am I right?