![]() Algunos de estos modelos utilizan series de tiempo, mientras que otros utilizan la función de similitud, que captura la forma cognitiva del razonamiento humano. Se realizó un comparativo de modelos que en la literatura ya han incorporado las tendencias de Google como variable explicativa. Keywords: Google trends Empirical similarity Aggregation levels Volatility, Exchange rateĮn este trabajo se muestra la ventaja de usar tendencias del motor de búsqueda de Google para pronosticar la volatilidad de corto plazo (semanal) del tipo de cambio peso mexicano - dólar estadounidense. Moreover, to the best of our knowledge, literature on the subject of using Google Trends to explain relevant economic variables is relatively scarce. We conclude that taking into account the Google Trends variable helps explains partially the behaviour of volatility and it is necessary to incorporate more aggregation levels. For example, an investor who needs to know the value that a variable will take in the future will take into account relevant, known, and available information, and weigh it to calculate the forecast. Some of the models are based on time series, whereas others are based on the similarity function, which captures the cognitive form of human reasoning. We perform a comparison of models in the literature that have used Google Trends to examine explanatory variables. We show the advantage of using Google search engine trends to forecast the volatility of the short-term (weekly) exchange rate between the Mexican peso and United States dollar.
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