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El objetivo de este trabajo es construir portafolios, a partir del cálculo de parámetros no condicionales (tradicionales) y condicionales, estos últimos mediante modelos Generalizados Autoregresivos con Heteroscedasticidad Condicional (GARCH) univariados y de Correlación Condicional Dinámica (GARCH-DCC), bajo una distribución t-Student. Una vez realizada la estimación con ambos tipos de parámetros, se comparan los portafolios tradicionales con respecto a los condicionales, determinando cuál de ellos resulta en estimar una mejor relación riesgo-rendimiento. Para el diseño de los portafolios óptimos se emplean los índices bursátiles del Mercado Integrado Latinoamericano (MILA) constituido por Colombia, Chile, México y Perú, precios de cierre diarios de enero 2017 a junio 2022. Los resultados señalan que, los portafolios condicionales tienen mejor desempeño que los portafolios construidos de manera tradicional. Los hallazgos tienen importantes implicaciones en términos del desarrollo de estrategias de inversión internacional en mercados emergentes.

This research aims to analyze whether the 42 category-specific Equity Market Volatility (EMV) trackers explain the US industrial production index (IPI) including the impact of the Covid-19 crisis. IPI values are forecasted, considering three scenarios of economic recuperation after the end of the Covid-19 pandemic. To achieve this purpose, first an Artificial Neural Network is employed to determine if the EMV categorical tracker elements explain Industrial Production. Once, the incidence of the EMV trackers on IPI is evidenced, an ARIMA model is used to forecast the Industrial Production Index from June 2021 to December 2022. Motivation for this research and its originality is the strong economic and financial links of the Mexican economy with the U.S. Economy. Current economic trends in Mexico, particularly its economic recovery partly linked to the U.S economic recovery.

The objective of this work is to test the overreaction hypothesis in the Mexican Stock Market for the period of 2002-2015, using monthly data and applying the Cumulative Average Residuals (CAR) methodology via the CAPM model and the three-factor model proposed by Fama and French. The CAR model is applied to test how winner and loser portfolios perform during the period under analysis. Overall, the evidence shows that average CAR for the loser portfolio is 0.706%, whereas CAR for the winner portfolio is 0.364%, and that are statistically different; nevertheless, both portfolios are co-integrated. This research contributes to the financial literature identifying overreaction in the Mexican Stock Market during the period examined.