80 research outputs found

    Identifying the evolution of stock markets stochastic structure after the euro

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    Previous studies have investigated the comovements of international equity markets by using correlation, cointegration, common factor analysis, and other approaches. In this paper, we investigate the stochastic structure of major euro and non-euro area stock market series from 1994 to 2006, by using cluster analysis techniques for time series. We use an interpolated-periodogram based metric for level and squared returns in order to compute distances between the stock markets. This method captures the stochastic dependence structure of the time series and solves the shortcoming of unequal sample sizes found for different countries. The clusters of countries are formed by the dendrogram and the principal coordinates associated with the sample spectrum for both the series of returns and volatilities. The empirical results suggest that the cross-country groups have become considerably more homogeneous with the introduction of the euro as an electronic currency. For reference, we also explore the pairwise correlations among the series

    Identifying common dynamic features in stock returns

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    This paper proposes volatility and spectral based methods for cluster analysis of stock returns. Using the information about both the estimated parameters in the threshold GARCH (or TGARCH) equation and the periodogram of the squared returns, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the "blue-chip" stocks used to compute the Dow Jones Industrial Average (DJIA) index.Asymmetric effects, Cluster analysis, DJIA stock returns, Periodogram, Threshold GARCH model, Volatility

    Some international evidence regarding the stochastic memory of stock returns

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    The present paper studies international stock indexes of the G-7 countries in the last 40 years. Evidence about the statistical memory of the returns is presented, and only in one country could the existence of long memory be sustained. These results contradict various previous studies that were based on the R/S analysis and consistently claimed the existence of long memory in financial returns. A general ARFIM A model capable of reproducing long- and short-memory properties is directly fitted to the data. The conclusion is then based on the estimated parameters of the model.info:eu-repo/semantics/publishedVersio

    Identifying common spectral and asymmetric features in stock returns

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    This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stock returns. Using the information about both the periodogram of the squared returns and the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the "blue-chip" stocks used to compute the Dow Jones Industrial Average (DJIA) index. For reference, we investigate also the similarities among stock returns by mean and squared correlation methods.Asymmetric effects; Cluster analysis; DJIA stock returns; Periodogram; Threshold ARCH model; Volatility

    Como apoiar e como abandonar os professores na batalha pelo sucesso educativo. A experiência de Portugal entre 1995 e 2020

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    Entre os anos finais do século XX e as duas primeiras décadas do século XXI, a educação em Portugal sofreu várias mudanças. Como resultado, a situação melhorou quase continuamente até 2015. Foi nesse ano que Portugal obteve os seus melhores resultados de sempre nas comparações internacionais PISA e TIMSS. Sofreu alguns reveses a partir de 2016, conforme revelaram os resultados PISA de 2018 e TIMSS de 2019. Este artigo analisa os principais fatores dessa evolução, destacando a capacitação dos professores para o bom desempenho das suas tarefas.Cómo apoyar y cómo abandonar a los profesores en la batalla por el éxito educativo. La experiencia de Portugal entre 1995 y 2020: Durante los últimos años del siglo XX y las dos primeras décadas del siglo XXI, la educación en Portugal sufrió varios cambios. Como resultado, su situación fue mejorando casi continuamente hasta el 2015. Siendo ese año en el que Portugal obtuvo los mejores resultados de su historia en las comparaciones internacionales PISA y TIMSS. Luego, sufrió algunos retrocesos a partir de 2016, como revelan los resultados de PISA 2018 y TIMSS 2019. En este artículo se analizan los principales factores de esta evolución, destacando la capacitación de los profesores para el buen desempeño de sus tareas.How to support and how to abandon teachers in the battle for educational success. The experience of Portugal between 1995 and 2020: Between the final years of the 20th century and the first two decades of the 21st century, education in Portugal underwent several changes. As a result, the situation improved almost continuously until 2015. That was the year when Portugal achieved its best results ever in the international PISA and TIMSS comparisons. It suffered some setbacks from 2016 onwards, as revealed by the 2018 PISA and 2019 TIMSS results. This article analyses the main factors of this evolution, highlighting the training of teachers to perform their tasks well.info:eu-repo/semantics/publishedVersio

    Improving a country’s education: PISA 2018 Results in 10 Countries

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    This book is probably one of the first to be published, or even the first, about the results of the Programme for International Student Assessment (PISA) 2018. It discusses how PISA results in ten different countries have evolved and what makes countries change. Information on each country’s educational system contextualizes the discussion about PISA and other Large-Scale International Assessments’ results, such as TIMSS, Trends in International Mathematics and Science Studies. One reason only made it possible for us to present this work to the reader with such a short delay after PISA results were published in December 2019: we were very fortunate to be able to gather an exceptionally knowledgeable and generous group of international experts. The ten countries discussed in this volume represent a wide variety of educational systems, from Australia and Taiwan, in the East, to England, Estonia, Finland, Poland, Portugal and Spain, in Europe, and to Chile and the USA, in the Americas. We have high-performing countries, countries that are around the OECD average, and countries that are struggling to attain the OECD average. Each country has its history that reflects efforts to improve educational achievement. The book is organized as follows. Each chapter is a data-based essay about the evolution of a specific country, discussed and supported by PISA results and other data, and represents the personal stance of the authors. Thus, each author represents his or her own views and not those from his or her institution or government. Each author draws on published data, as well as on a vast set of information and supports his or her view with data and reliable information. The introductory chapter gathers my reading of the ten chapters. It follows the same principles: I express my views freely, but support them with the best information available. I do not claim to voice the opinion of the authors, and I am the sole responsible for what I wrote. A final chapter introduced following a Springer referee suggestion provides the necessary background in order to understand what PISA measures and how. It shows examples of PISA and TIMSS questions that convey a better idea on what the results of these surveys mean about students’ knowledge and skills. I am honored to edit this book, and I am sure it will be useful to all those interested in understanding what it takes to improve a country’s education system.info:eu-repo/semantics/publishedVersio

    Discrimination between deterministic trend and stochastic trend processes

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    Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial autocorrelation and periodogram based metrics.Autocorrelation; Classification; Determinist trend; Kullback-Leibler; Periodogram; Stochastic trend; Time series

    Persistence in portuguese economic activity

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