Afluencia de capitales externos y políticas macroeconómicas

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La crisis del peso mexicano

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Estudio Económico de América Latina y el Caribe 2016: La Agenda 2030 para el Desarrollo Sostenible y los desafíos del financiamiento para el desarrollo

En su edición número 68, que corresponde al año 2016, el Estudio Económico de América Latina y el Caribe consta de tres partes. En la primera se resume el desempeño de la economía regional en 2015 y se analiza la evolución durante el primer semestre de 2016, así como las perspectivas de crecimiento para el año. Se examinan los factores externos e internos que han incidido en el desempeño económico de la región y se destacan algunos de los desafíos para las políticas macroeconómicas en un contexto externo caracterizado por el bajo crecimiento y elevados grados de incertidumbre. En la sección temática de este Estudio se analizan los desafíos que tienen los países de América Latina y el Caribe en el ámbito interno y externo para movilizar el financiamiento del desarrollo. En lo interno, la desaceleración del crecimiento y las mayores restricciones fiscales imponen importantes retos a la movilización de recursos. En lo externo, la condición de países de renta media dificulta el acceso al financiamiento externo concesionado o de la cooperación internacional. La tercera parte, que está disponible en la página web de la CEPAL (www.cepal.org), contiene las notas referentes al desempeño económico de los países de América Latina y el Caribe en 2015 y el primer semestre de 2016, así como los respectivos anexos estadísticos. La información que se presenta ha sido actualizada al 30 de junio de 2016.

Economic Survey of Latin America and the Caribbean 2016: The 2030 Agenda for Sustainable Development and the challenges of financing for development

This, the sixty-eighth edition of the Economic Survey of Latin America and the Caribbean, which corresponds to the year 2016, consists of three parts. Part I outlines the region’s economic performance in 2015 and analyses trends in the first half of 2016, as well as the outlook for the rest of the year. It examines the external and internal factors influencing the region’s economic performance and highlights some of the macroeconomic policy challenges that have arisen in an external context of weak growth and high levels of uncertainty. Part II analyses the challenges that the countries of Latin America and the Caribbean face at the domestic and international levels in mobilizing financing for development. On the domestic front, slower growth and tighter fiscal restrictions pose significant challenges for the mobilization of resources. Externally, the classification of many of the region’s countries in the middle-income category limits their access to concessional external financing or international support. Part III of this publication may be accessed on the web page of the Economic Commission for Latin America and the Caribbean (www.eclac.org). It contains the notes relating to the economic performance of the countries of Latin America and the Caribbean in 2015 and the first half of 2016, together with their respective statistical annexes. The cut-off date for updating the statistical information in this publication was 30 June 2016.

Estudo Econômico da América Latina e do Caribe 2016: A Agenda 2030 para o Desenvolvimento Sustentável e os desafios do financiamento para o desenvolvimento. Documento informativo

Em sua edição número 68, que corresponde a 2016, o Estudo Econômico da América Latina e do Caribe consta de três partes. A primeira resume o desempenho da economia regional em 2015 e analisa a evolução durante o primeiro semestre de 2016, bem como as perspectivas de crescimento para o ano. Examina os fatores externos e internos que incidiram no desempenho econômico da região e destaca alguns dos desafios para as políticas macroeconômicas num contexto externo caracterizado por baixo crescimento e elevados graus de incerteza. A seção temática deste estudo analisa os desafios que os países da América Latina e do Caribe enfrentam no âmbito interno e externo para mobilizar o financiamento para o desenvolvimento. No âmbito interno, a desaceleração do crescimento e as maiores restrições fiscais impõem importantes desafios à mobilização de recursos. No âmbito externo, a condição de países de renda média dificulta o acesso ao financiamento externo concessional ou à cooperação internacional. A terceira parte, que está disponível no site da CEPAL (www.cepal.org), contém as notas referentes ao desempenho econômico dos países da América Latina e do Caribe em 2015 e no primeiro semestre de 2016, bem como os respectivos anexos estatísticos. A informação apresentada foi atualizada em 30 de junho de 2016.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Tender offers in South America: Are abnormal returns really high?

Diferentes estudios realizados en mercados de capital desarrollados han revelado tasas de retorno positivas inusuales de por lo menos 15% durante la fecha de anuncio de la oferta pública de adquisición de acciones. Aunque casi no se han llevado a cabo estudios sobre los mercados bursátiles en Sudamérica, algunos estudios han reportado tasas de retorno positivas inusuales en un rango del 25% al 50%, las cuales están relacionadas con el anuncio de la primera oferta de adquisición. En el presente estudio, se argumenta que las tasas de retorno positivas inusuales estimadas en los mercados emergentes son altas porque los estudios se han basado en un mercado de capitales totalmente segmentado aplicando el modelo de mercado y utilizando un índice del mercado bursátil local. Al considerar la integración parcial entre los cinco mercados emergentes en Sudamérica, se demuestra que efectivamente existen tasas de retorno positivas inusuales antes, durante y después de la fecha de anuncio de la primera oferta de adquisición. Sin embargo, el retorno positivo inusual asociado a la fecha del anuncio se encuentra en el orden del 8%. Utilizando un modelo de mercado que considere la integración parcial y el riesgo a la baja, se obtiene una tasa de retorno inusual ligeramente mayor. Estos resultados señalan una menor tasa de retorno positiva inusual en la muestra de las empresas sudamericanas incluidas en el estudio.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Las implicaciones macroeconómicas de las nuevas regulaciones bancarias con respecto al capital en mercados emergentes: un modelo de duopolio adaptado a bancos aversos al riesgo.

Con el propósito de analizar el impacto de la nueva regulación de capital de bancos (Basilea II) sobre el ciclo económico de una economía emergente, desarrollo un modelo de duopolio compuesto por bancos locales y extranjeros. Los principales resultados son: por medio de la nueva regulación de capital, la evaluación del riesgo crediticio realizada por un banco internacional en un país, no sólo afecta a los préstamos totales de ese país sino también a los activos totales otorgados en otros países. Segundo, cuando los bancos son aversos al riesgo y a medida que la diversificación del portafolio aumenta, el cambio en los préstamos concedidos en un país por un banco internacional como proporción de la inversión inicial, así como el nivel de los prestamos totales de ese país, pueden resultar fuertemente afectados por el comportamiento de un banco que sigue sólo “noticias” a través de la nueva regulación de capital. Finalmente, incluso cuando la diversificación del portafolio crece sin  límite, la implicación macroeconómica de un cambio en la estimación del riesgo crediticio debida a la nueva regulación de capital, aumenta a medida que los bancos son menos aversos al  riesgo.