NETWORK ANALYSIS OF THE MEXICAN STOCK MARKET
This study investigates the dynamics of equity networks in Mexico from 2018 to 2023, focusing on the impact of the COVID-19 pandemic. Methodological steps include calculating stock returns, estimating annual GARCH models, constructing lower-tailed dependency matrices, and forming networks based on t...
| Autor: | |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Investigación Económica |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/87209 |
| Acceso en línea: | https://www.revistas.unam.mx/index.php/rie/article/view/87209 |
| Access Level: | acceso abierto |
| Palabra clave: | Multivariate time series networks GARCH GNAR |
| Sumario: | This study investigates the dynamics of equity networks in Mexico from 2018 to 2023, focusing on the impact of the COVID-19 pandemic. Methodological steps include calculating stock returns, estimating annual GARCH models, constructing lower-tailed dependency matrices, and forming networks based on these matrices. The characteristics of the resulting networks are described. In addition, 10,000 Erdos-Reyni simulations are performed to estimate GNAR models up to order two, selecting the best estimates according to AIC, BIC, and llk criteria. The predictive performance of GNAR models compared to univariate AR and VAR models is evaluated. These stages help to better understand the interconnection between Mexican financial markets, offering valuable insights for risk management and decision-making. |
|---|