paper-with-me

Papers

Completing correlation matrices

2021-11-24 · Olaf Dreyer, Horst Köhler, Thomas Streuer

We describe a way to complete a correlation matrix that is not fully specified. Such matrices often arise in financial applications when the number of stochastic variables becomes large or when several smaller models are combined in a larger model. We argue that the proper completion to consider is the matrix that maximizes the entropy of the distribution described by the matrix. We then give a way to construct this matrix starting from the graph associated with the incomplete matrix. If this graph is chordal our construction will result in a proper correlation matrix. We give a detailed description of the construction for a cross-currency model with six stochastic variables and describe extensions to larger models involving more currencies.

📄 PDF Abstract BibTeX arXiv:2111.12640

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm

2024-02-08 · M. Mijaíl Martínez-Ramos, Parisa Majari, Andres R. Cruz-Hernández, Hirdesh K. Pharasi 외

We analyze correlation structures in financial markets by coarse graining the Pearson correlation matrices according to market sectors to obtain Guhr matrices using Guhr's correlation method according to Ref. [P. Rinn {\…

Targeted matrix completion

2017-04-30 · Natali Ruchansky, Mark Crovella, Evimaria Terzi

Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix …

Matrix CompletionRecommendation Systems

An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices

2015-02-11 · Cheng Zhou, Fang Han, Xinsheng Zhang, Han Liu

There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of large rank-based correlation matrices sinc…

valid

A New Method for Generating Random Correlation Matrices

2022-10-15 · Ilya Archakov, Peter Reinhard Hansen, Yiyao Luo

We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, gamma = g(C), which maps any distribution…

Generating Correlation Matrices with Graph Structures Using Convex Optimization

2025-02-25 · Ali Fakhar, Kévin Polisano, Irène Gannaz, Sophie Achard

This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibil…