arXiv Machine Learning By Xavier Fonseca

The Decision Geometry of Covariance Estimation for the Global Minimum-Variance Portfolio under Heavy Tails

Read the original on arXiv Machine Learning →

arXiv:2606. 27462v1 Announce Type: cross Abstract: The global minimum-variance portfolio (GMVP) is the canonical decision built from an estimated covariance matrix, yet covariance estimators are universally evaluated by matrix-norm loss, which is not the object the decision depends on.

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arXiv Machine Learning
Jul 22

Optimizing Regret

arXiv:2607. 18866v1 Announce Type: cross Abstract: Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops the complete derivative theory of the covariance regret functional.

By Irene Aldridge
Hugging Face Trending Papers
Jul 21

Optimizing Regret

Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops the complete derivative theory of the covariance regret functional. We derive the Gâteaux derivative, showing that the universal steepest-descent direction is the contrarian policy $-(c-\bar{c})$, while ascent yields momentum.