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Resumen
Este estudio investiga la dinámica de volatilidad y las correlaciones variables en el tiempo entre Bitcoin (BTC) y los principales mercados financieros y de materias primas, incluidos el oro, el petróleo, el NASDAQ, el NIKKEI, el FTSE, el DAX y el índice del dólar estadounidense (USDINX). Utilizando datos diarios y modelos de la familia GARCH, se cuantificó la persistencia, la asimetría y la memoria a medio plazo en la volatilidad del BTC. La selección de modelos mediante los criterios de log-verosimilitud, SIC y AIC identifica al modelo EGARCH como el más adecuado para capturar el comportamiento de la varianza condicional. Posteriormente, se empleó un marco DCC-MGARCH para estimar las correlaciones del mercado transversal en evolución. Los resultados indican que la volatilidad del BTC es altamente persistente, presenta reacciones más fuertes ante choques negativos y muestra una reversión moderada a la media. El oro presenta la menor persistencia, lo que confirma su papel como activo diversificador estable. Las estimaciones del DCC-MGARCH revelan correlaciones positivas débiles entre BTC y oro, correlaciones negativas entre BTC y USDINX y ausencia de vínculos significativos entre BTC y
petróleo o BTC y DAX, lo que implica un considerable potencial de diversificación. Cabe destacar que las correlaciones BTC-NIKKEI se fortalecieron durante el período de la COVID-19, mientras que las correlaciones BTC-oro aumentaron de forma moderada. Estos hallazgos subrayan la importancia de estrategias dinámicas de cartera: los pesos óptimos cambian con la evolución de las covarianzas condicionales, lo que hace que las asignaciones estáticas sean subóptimas.
Para los responsables de políticas, la persistencia de la volatilidad y los umbrales de correlación
pueden orientar los límites de apalancamiento y exposición, especialmente cuando los vínculos
de BTC con los activos tradicionales se intensifican.
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