Learning Undirected Graphs in Financial Markets

Jos\'e Vin\'icius de Miranda Cardoso and Daniel P. Palomar

Papers from arXiv.org

Abstract: We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to the conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets. In addition, we propose algorithms to learn undirected graphs that account for stylized facts and tasks intrinsic to financial data such as non-stationarity and stock clustering.

Date: 2020-05, Revised 2020-11
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Citations: View citations in EconPapers (1)

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