Covariance Puns

Hi, I have a basic background in stats and wrote a script to screen for volume activity in stocks that have high covariance. Unsurprisingly, most tickers had a very low covariance in volume being less than 0.1.

However, I stumbled across two seemingly unrelated tickers with a covariance in volume % daily change of 0.88 in 2021. Is there a way to calculate how likely this is due to chance?

Also, if there are any recommendations for resources on how to do this type of analyses the right way please let me know. My goal is just to identify signals that could suggest certain future behavior. Thanks!

Hi, I’m trying to solve a question: Let N(t) be a poisson process with constant intensity lambda on R.

What is the covariance between N(s) and N(t)?

Now I have the solutions, and I know I must use cov(xy) = E(xy) - E(x)E(y), and then in the solutions E(N(s)N(t)) is found by saying E(N(s)N(t)) = E(N(s)^2) + E(N(s)(N(t)-N(s))) Not sure how this last part is got, can’t find any notes on it. Any help would be greatly appreciated.

I'd like to generate data, in Python or Matlab, from a 5-component GMM (Gaussian Mixture Model) distribution.

In Matlab "Statistics and Machine Learning Toolbox" I found gmdistribution. Its syntax is:

gm = gmdistribution(mu,sigma,p)

I already have 5 values of mean, 5 values of variance and 5 weights.

But from what I understand, to generate GMM distribution, covariance matrix is necessary.

So, my data seems to be not sufficient (I guess I have to calculate covariances from raw data).

Or, maybe, covariances could be calculated from means and variances?

I understand that you can obtain the standard errors of the coefficients of a linear regression model by taking the square root of the diagonal elements of the variance-covariance matrix. For some reason, I just can't seem to see why that works. My intuition tells me that this should be really easy to grasp but apparently my brian is currently MIA. Can anybody ELI5 this to me please? Thanks!

Hello!

Does anybody tried to calculate covariance matrix for all stocks, bonds, commodities, crypto in the world?

Hello everyone.

This isn't a homework. I've been working on finding a better way to embed a drug molecule for deep learning model. A paper that I've been following recently uses MD, but I want to improve on it.

My question specifically is, does statistics have an algorithm that can best embed a drug molecule into a vector or scalar aside from MD and Euclidean distances?

Hi, I am doing a non-linear least-squares fit and was calculating the standard errors from the diagonal of the covariance matrix.

I also am calculating the standard errors using the bootstrap method. The residuals look normal to me.

The bootstrap method results in standard errors that are roughly 4-5X that from the covariance matrix. What do I make out of this? I was expecting them to be close, especially that the residuals look normal.

Hi guys. I have some trouble understanding the meaning / implication of covariance stationary. The whole concept seems blurry to me. I think I kinda get the "shallow" idea of what it is but can't concretely form it in my head. It doesn't help me visualize what is a covariance stationary on a graph nor it shows the situation where it's violated

LOS 6f introduces mean reversion. It says mean-reverting level is x = b0 / (1-b1). So if we want a FINITE mean-reverting level, |b1| < 1. If |b1| = 1, it's a unit root and undefined mean-reverting level. But what if |b1| > 1? According to the math, it still gets us a FINITE mean-reverting level (therefore covariance stationary). But I can't find any text that really confirms my understanding

And of course then comes unit root. OK...why t test doesn't work but first-differencing work...?

Note that all of the above comes from my understanding of Schweser textbook. I haven't read CFA curriculum yet since I just finished L1 exam and wanted a head start. It's a bit frustrating to me how a logical question (what about |b1| > 1) is just left unanswered. Even the recently analystprep video doesn't show the scenario. It just seems everyone doesn't understand what they are talking about...

Anyway, my main question is about the |b1| > 1. You can discuss other issues I mentioned as well

Thank you

Hey everyone, I’m trying to find the covariance between 3 stocks Im analyzing. In short, rather than type out the covariance formula 3 times to satisfy all pairings of my 3 stocks, I was hoping there would be a way to dedicate two cells as lists that refer to the arrays of data. For example, cells c2-c20 belong to Ford, cells d2-d20 belong to Nike etc..

I was able to use the formula manager to define the 3 separate ranges with their names, however I can’t find a way to put the named ranges in a list which could then be referred to in the covariance formula

Any help/feedback/criticism is appreciated!

Ive heard it described as the strength of linear association, but what about it makes it obviously about linear associations? To me at least its not exactly obvious just looking at the formula.

Hi, everyone. I am doing a research on this topic for an application in the autonomous driving field and now a doubt came to my mind because I have found a lot of papers discussing about the estimations, but there is a gap about the covariances: Should the covariances of this both filters be similar when the system is quite nonlinear? Another question is if I do an ellipse based on the covariance of the position coordinates, should the angle of this ellipse follows the vehicle's orientation?

I am testing concordance between boys and girls on their rankings of 9 items. The literature says this can be done by comparing their mean rank vectors under the null hypothesis that the mean rank are equal. The test statistics requires the covariance matrix S, which is an 8x8 dim estimated covariance matrix of the rank vectors of the 2 groups. Any idea how I estimate S, given the mean rank vector of the boys' and the girls' groups? Thanks

Might be a fundamentally basic question but I am confused since I could not find one place where I could find the difference between covariance, correlation, variance and standard deviation.

An example to understand their purpose would be of great help.

Thanks a ton!!!

I am interested in understanding why the covariance is defined the way it is, that is:

E((X-ux)(Y-uY))

Why/how does the above equation give us the degree of association?

Hope makes sense. I am always interested in understanding where things come from. But is this a case of because it is defined that way...