Lest you feel that I hate women, I don’t. They are both angelic and demonic, like me. I was just stupid and young and did not think women that I love would have the vampyre demon in them to use me and destroy my life and pretend that it’s ok and not have to pay. Anyway, it’s all water under the bridge and I have recovered my senses enough not to smash any faces with baseball bats out of anger. In fact, I am an Angel of Mercy for never doing any physical harm. Many people deserve to have their faces bust with a baseball bat. Instead, I’ve been angelic enough to use this immense reservoir of anger for something decent. I’ll create a new science of finance out of actual market data and new mathematical ideas and forget the bitches. They can dig their own graves seeking happiness when they cannot love. Fuck them all. Fucking whores and cunts all of the lot. Once in a while you think you’ve met someone different and kaboom, reality hits you like a snap — nope another one bites the dust.

Then you want to create the graph as follows:

A<-abs(C-diag(C))-0.5*sd(C)

This will give you a denoised covariance matrix (assuming 0.5*sd(C) is noise without a diagonal which is perfect for drawing edges between assets and essentially giving us the path of spread of the poison of money.

E<-eigen(H)

Now the simple way of getting the original data into components of laplacian eigenvectors is

r<-dim(m)[1]

x<-expand.dyadic(m[r,])

and now we have an object which we want to fit a DYNAMICAL SYSTEM model that is analogous to the Navier-Stokes equation. We know that physicists have dicovered cascading and scaling and other effects in their massive tick-by-tick analyses. This is a different perspective. This uses free daily data with a GLOBAL perspective. Our aim is to discover dynamic models of the global volatility using the space variable we created through this market graph.

This is still a conjectural approach, but it would be very good to have theory for this in multiple directions. David Donoho has pointed me to the statistical models of graphs themselves by Patrick Wolfe and others. These statistical models of graphs are very very useful to modeling volatility dynamics in a global scale because they tell us about infinite n limits in assets. Another issue of interest is the dynamics of quasilinear parabolic equaitons (Laplacian is just a matrix on a graph so these are ODE systems in n variables on a graph) with a nonlinear convection term like power laws. You can find these in this good book:

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