Timeline for How can I see a covariance matrix, as a standard regression y-Bx
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S Aug 19 at 19:23 | history | suggested | cottontail | CC BY-SA 4.0 |
improved mathjax format
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Aug 19 at 18:33 | review | Suggested edits | |||
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Jul 3 at 2:52 | history | edited | User1865345 | CC BY-SA 4.0 |
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Sep 12, 2016 at 23:48 | answer | added | Andrew Beaven | timeline score: 0 | |
Sep 5, 2016 at 20:03 | comment | added | domenico | ok then the initial statement in the paper of arxiv.org/pdf/0708.0046.pdf is confusing me " We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem." but I do not get their formulation. that is why I asked it here.. | |
Sep 5, 2016 at 19:51 | comment | added | shadowtalker | Neither OLS nor Lasso is involved here. Adding a penalty term does not turn this into a regression problem. | |
Sep 5, 2016 at 18:15 | comment | added | domenico | ok, I am showing the difference between L1norm and L2 norm and why I am applying lasso (when covariance matrix is singular ect ect) instead of OLS.. but how do i show the difference between lasso and ols - applied to a covariance matrix? I do not know how to translate the relationship given my data into a quadratic objective function.. I have been researching in this topic only since 2 weeks so I am no expert and do not know how to move from a to b.. | |
Sep 5, 2016 at 17:32 | comment | added | Mark L. Stone | Why do you want to see the covariance matrix as a regression? Why do you think it should be? Forget about regression. You can add a penalty based on risk to the objective function if you want to .If anything the relationship is that regression can also be formulated as an optimization problem with a quadratic objective function, to which a penalty term can also be applied if desired. | |
Sep 5, 2016 at 17:25 | history | edited | domenico |
edited tags
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Sep 5, 2016 at 17:25 | review | First posts | |||
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Sep 5, 2016 at 17:20 | history | asked | domenico | CC BY-SA 3.0 |