Abstract
Our goal in this paper is to study and characterize the interdependency structure of the Mexican Stock Exchange (mainly stocks from Bolsa Mexicana de Valores) for the period 2000-2019 which provide a one shot big-picture panorama. To this end, we estimate correlation/concentration matrices from different models and then compute centralities and modularity from network theory.
Highlights
In this paper we investigate the interdependency structure of daily returns in the Mexican stock exchange market
There are different methods of estimating a covariance/correlation/concentration matrix and we have selected a estimation based on a specific class of Markovian Random Fields (MRF) which in the statistical literature is well known under the name Gaussian Graphical models (GGm)
Whether the negative relationship found in [23] holds true for partial correlations can be the subject of future research, we find that the IPC index has high degree- and eigen- centralities for networks based on Tail-dependence and Pearson correlation matrices
Summary
In this paper we investigate the interdependency structure of daily returns in the Mexican stock exchange market. To this end, we build a database of free and publicly available time series of main stocks for the period 2000-2019 and conduct our study in stages that are put together to give a unified treatment to our main topic of interest here which is the interdependency structure of daily log-returns in the Mexican stock exchange. In the first stage we focus on the estimation of partial correlations of log returns of daily prices. A lasso-regularized estimation is useful in this context which is inbuilt in the estimation of a GGm. Third, we want an estimation that filters out a “noisy” correlation selecting only clear relationships between two series, again this is provided by the lasso-regularized
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