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High and low prices are frequently used in many contexts, such as estimating volatility. See, for example, the Garman-Klass and Yang-Zhang estimators. Brandt and Kinlay provide a nice summary of some of these estimators. However, it sounds like you are more interested in using high/low information for evaluating whether mean reversion has taken place. In ...


A multivariate normal distribution can be thought of as normal margins with a normal copula. The multivariate t is the same way, but it has t margins with a t copula and they all have the same degrees of freedom. So it has t copula dependence. It is either a spherical or an elliptical distribution. I can't think of a good reason to use a multivariate t. The ...


Since pentahedrons are 3d shapes, but there is no reason to think currencies live in a 3d world, you can just treat the 'pentahedron' as a weighted node graph of the 5 currencies. A graph edge from one currency to another represents an exchange of those two currencies. So in the same fashion as usual vectors, I can go from currency A to C via B by executing ...


Yes you can, how depends fully on your required accuracy and also whether PC1 and PC2 are sufficient in explanatory power of the log differences of your futures contract. Also, make sure you understand the signs of the eigenvalues (sign of the PC) can be different from one experiment to the next as they are arbitrary (the values are obviously not). Here ...


Your question is formulated in a very general way, this is why any answer will need to be general as well. In a nutshell and in full generality you need to estimate the joint distribution from your historical data since in most cases correlations alone are not sufficient to define the joint distribution. In a second step you can calculate the distribution ...


I found a link and I have to repeat: I don't think that PCA helps you to find a price ... it helps to model the movements of prices but not their values. You get something like a factor model ... this does not directly give you a price ... maybe you also want to have a look at this link where PCA is applied to the oil market.


Why not fit an ARMA model to the rolling returns first, and then model the residuals in your regression equation? That way you should be removing most of the effects of auto-correlation.

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