Previsão não-linear da taxa de câmbio real/dólar utilizando redes neurais e sistemas nebulosos

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Sócio-Econômico. Programa de Pós-graduação em Economi

OTIMIZAÇÃO DE CARTEIRAS BASEADA EM MODELOS DE CORRELAÇÕES CONDICIONAIS

Multivariate volatility models based on conditional correlations have been receiving increased attention due to the possibility of modeling the dynamics in the covariance matrices of financial asset returns. In this paper, the performance of this class of models is analyzed when applied to the problem of optimal portfolio selection of a portfolio of stocks. For that purpose, it is implemented four alternative parsimonious specifications for the class of conditional correlation Garch models. Using data from the Brazilian and North-American stock markets, it is obtained optimal minimum variance portfolios for each of the markets. The results indicate that the minimum variance portfolios obtained with conditional correlation models achieved superior results in terms of lower risk, higher risk-adjusted returns, higher cumulative gross returns and higher cumulative excess returns when compared to the main stock indices of each market.Modelos multivariados de volatilidade baseados em correlações condicionais têm recebido grande atenção em função da sua capacidade de modelar a dinâmica presente nas matrizes de covariâncias de retornos de ativos financeiros. Neste artigo, analisa-se o desempenho dessa classe de modelos quando aplicados ao problema de seleção de otimização de carteiras de ações. Para isso, implementam-se quatro diferentes especificações parcimoniosas de modelos de correlações condicionais da família Garch. Utilizando-se dados dos mercados acionários brasileiro e norte-americano, obtêm-se carteiras de mínima variância para cada um dos mercados. Os resultados indicam que as carteiras de variância mínima obtidas com modelos de correlações condicionais obtiveram resultados superiores em termos de menor risco, maior retorno ajustado ao risco, maior retorno bruto acumulado e maior excesso de retorno acumulado quando comparados aos principais índices de referência de cada um dos países

Multivariate volatility models in financial risk management and portfolio selection

Multivariate volatility modeling is now established as one of the most influential and challenging areas in financial econometrics. Rather than modeling assets separately in a traditional univariate way, research in econometric modeling of volatility has been evolving towards the extension of the univariate framework through the development of multivariate specifications able to model and predict the temporal dependence in the second-order moments of many assets in a portfolio or in different markets taking into account their correlated behavior. Therefore, the use of multivariate volatility models in quantitative risk management has gained increased importance among academics and practitioners concerned with measuring and managing financial risks. In this thesis we study multivariate volatility models in problems involving quantitative market risk measurement and management. First, we consider the risk measurement problem of forecasting value-at-risk (VaR) using multivariate models vis-à-vis traditional univariate models in problems involving diversified portfolios with a large number of assets. Second, we present a novel active risk management approach based on current regulatory criteria to select optimal portfolio compositions. Finally, I discuss the implications, advantage and caveats of using multivariate volatility models, and propose research lines that can contribute to guide further research in this are

Comparing univariate and multivariate models to forecast portfolio Value-at-Risk

This article compares multivariate and univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast portfolio value-at-risk (VaR). We provide a comprehensive look at the problem by considering realistic models and diversified portfolios containing a large number of assets, using both simulated and real data. Moreover, we rank the models by implementing statistical tests of comparative predictive ability. We conclude that multivariate models ou tperform their univariate counterparts on an out-of-sample basis. In particular, among the models considered in this article, the dynamic conditional correlation model with Student's t errors seems to be the most appropriate specification when implemented to estimate the VaR of the real portfolios analyzedA. A. P. S. acknowledges financial support from research Projects CNPq Universal 481719/2011-3 and UFSC-Funpesquisa 2010/2011 from the Brazilian Government. F. J. N. is supported by the Spanish Government through Project MTM2010-16519. E. R. is supported by the Spanish Government ECO2009-0810

ULTRASTRUCTURAL CHANGES IN Schistosoma mansoni MALE WORMS AFTER in vitro INCUBATION WITH THE ESSENTIAL OIL OF Mentha x villosa Huds

Introduction: The essential oil Mentha x villosa (MVEO) has a wide range of actions, including antibacterial, antifungal, antiprotozoal and schistosomicidal actions. The present study aimed to investigate the ultrastructural changes of MVEO on the tegument of adult Schistosoma mansoni. Materials and Methods: Different concentrations of MVEO were tested on S. mansoni adult worms in vitro. Ultrastructural changes on the tegument of these adult worms were evaluated using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results: The MVEO caused the death of all worms at 500 μg mL-1 after 24 h. After 24h of 500 μg mL-1 MVEO treatment, bubble lesions were observed over the entire body of worms and they presented loss of tubercles in some regions of the ventral portion. In the evaluation by TEM, S. mansoni adult worms treated with MVEO, 500 μg mL-1, presented changes in the tegument and vacuoles in the syncytial matrix region. Glycogen granules close to the muscle fibers were visible. Conclusion: The ability of MVEO to cause extensive ultrastructural damage to S. mansoni adult worms correlates with its schistosomicidal effects and confirms earlier findings with S. mansoni

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

OTIMIZAÇÃO DE CARTEIRAS BASEADA EM MODELOS DE CORRELAÇÕES CONDICIONAIS

Multivariate volatility models based on conditional correlations have been receiving increased attention due to the possibility of modeling the dynamics in the covariance matrices of financial asset returns. In this paper, the performance of this class of models is analyzed when applied to the problem of optimal portfolio selection of a portfolio of stocks. For that purpose, it is implemented four alternative parsimonious specifications for the class of conditional correlation Garch models. Using data from the Brazilian and North-American stock markets, it is obtained optimal minimum variance portfolios for each of the markets. The results indicate that the minimum variance portfolios obtained with conditional correlation models achieved superior results in terms of lower risk, higher risk-adjusted returns, higher cumulative gross returns and higher cumulative excess returns when compared to the main stock indices of each market.Modelos multivariados de volatilidade baseados em correlações condicionais têm recebido grande atenção em função da sua capacidade de modelar a dinâmica presente nas matrizes de covariâncias de retornos de ativos financeiros. Neste artigo, analisa-se o desempenho dessa classe de modelos quando aplicados ao problema de seleção de otimização de carteiras de ações. Para isso, implementam-se quatro diferentes especificações parcimoniosas de modelos de correlações condicionais da família Garch. Utilizando-se dados dos mercados acionários brasileiro e norte-americano, obtêm-se carteiras de mínima variância para cada um dos mercados. Os resultados indicam que as carteiras de variância mínima obtidas com modelos de correlações condicionais obtiveram resultados superiores em termos de menor risco, maior retorno ajustado ao risco, maior retorno bruto acumulado e maior excesso de retorno acumulado quando comparados aos principais índices de referência de cada um dos países

Os pesquisadores, as publicações e os periódicos da área de Finanças no Brasil : uma análise com base em currículos da plataforma Lattes

Este artigo analisa a produc¸ ˜ao cient´ıfica da ´area de Financ¸as no Brasil. Utilizando um software propriet´ario para obter informac¸ ˜oes diretamente da plataforma de curr´ıculos Lattes foi poss´ıvel verificar o perfil e as tendˆencias da pesquisa em Financ¸as no territ´orio nacional. Os principais resultados da pesquisa mostram que a maioria dos pesquisadores de Financ¸as s˜ao relativamente jovens em termos de tempo de carreira, com doutorado finalizado entre os anos de 2005 e 2014 e situados no sudeste do pa´ıs. Observa-se tamb´em que a produc¸ ˜ao cient´ıfica nacional em peri´odicos internacionais de impacto ´e pequena em comparac¸ ˜ao com o total de publicac¸ ˜oes encontradas. O n´umero de publicac¸ ˜oes por ano tem crescido exponencialmente, por´em a qualidade das produc¸ ˜oes, medida pelo Qualis, deteriorou-se. Uma an´alise da produtividade dos autores mostra que os autores mais produtivos possuem duas caracter´ısticas em comum: doutorado fora do Brasil e bolsa de produtividade do CNPQ.This paper analyzes the scientific output of Finance researchers in Brazil. Using a proprietary software to download information directly from the Lattes platform it was possible to verify the profile and the tendencies of research in the area of Finance in the national territory. The main results of the study show that most of the researchers of Finance are relatively young with respect to their career, with PhD finished in between the years of 2005 and 2014, and located in the southeastern part of the country. The scientific output of Brazilian researchers in international journals is small in comparison to the total of publications. The number of published papers has risen exponentially, however the quality of the papers, measured by Qualis, has decreased. An analysis of the productivity of the researchers show that the most productive authors have two common attributes: PhD degree obtained in a foreign institution and the productivity scholarship from CNPQ