In search of exchange rate predictability : a study about accuracy, consistency, and granger causality of forecasts generated by a Taylor Rule Model

Este estudo investiga o poder preditivo fora da amostra, um mês à frente, de um modelo baseado na regra de Taylor para previsão de taxas de câmbio. Revisamos trabalhos relevantes que concluem que modelos macroeconômicos podem explicar a taxa de câmbio de curto prazo. Também apresentamos estudos que são céticos em relação à capacidade de variáveis macroeconômicas preverem as variações cambiais. Para contribuir com o tema, este trabalho apresenta sua própria evidência através da implementação do modelo que demonstrou o melhor resultado preditivo descrito por Molodtsova e Papell (2009), o “symmetric Taylor rule model with heterogeneous coefficients, smoothing, and a constant”. Para isso, utilizamos uma amostra de 14 moedas em relação ao dólar norte-americano que permitiu a geração de previsões mensais fora da amostra de janeiro de 2000 até março de 2014. Assim como o critério adotado por Galimberti e Moura (2012), focamos em países que adotaram o regime de câmbio flutuante e metas de inflação, porém escolhemos moedas de países desenvolvidos e em desenvolvimento. Os resultados da nossa pesquisa corroboram o estudo de Rogoff e Stavrakeva (2008), ao constatar que a conclusão da previsibilidade da taxa de câmbio depende do teste estatístico adotado, sendo necessária a adoção de testes robustos e rigorosos para adequada avaliação do modelo. Após constatar não ser possível afirmar que o modelo implementado provém previsões mais precisas do que as de um passeio aleatório, avaliamos se, pelo menos, o modelo é capaz de gerar previsões “racionais”, ou “consistentes”. Para isso, usamos o arcabouço teórico e instrumental definido e implementado por Cheung e Chinn (1998) e concluímos que as previsões oriundas do modelo de regra de Taylor são “inconsistentes”. Finalmente, realizamos testes de causalidade de Granger com o intuito de verificar se os valores defasados dos retornos previstos pelo modelo estrutural explicam os valores contemporâneos observados. Apuramos que o modelo fundamental é incapaz de antecipar os retornos realizados.

Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)

O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de “precisão” da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.

A Higher order and stable method for the numerical integration of Random Differential Equations

Trabalho apresentado no Congresso Nacional de Matemática Aplicada à Indústria, 18 a 21 de novembro de 2014, Caldas Novas - Goiás

Short-term temporal changes of soil carbon losses after tillage described by a first-order decay model

Tillage stimulates soil carbon (C) losses by increasing aeration, changing temperature and moisture conditions, and thus favoring microbial decomposition. In addition, soil aggregate disruption by tillage exposes once protected organic matter to decomposition. We propose a model to explain carbon dioxide (CO2) emission after tillage as a function of the no-till emission plus a correction due to the tillage disturbance. The model assumes that C in the readily decomposable organic matter follows a first-order reaction kinetics equation as: dC(sail)(t)/dt = -kC(soil)(t) and that soil C-CO2 emission is proportional to the C decay rate in soil, where C-soil(t) is the available labile soil C (g m(-2)) at any time (t). Emissions are modeled in terms soil C available to decomposition in the tilled and non-tilled plots, and a relationship is derived between no-till (F-NT) and tilled (F-Gamma) fluxes, which is: F-T = a1F(NT)e(-a2t), where t is time after tillage. Predicted and observed fluxes showed good agreement based on determination coefficient (R-2), index of agreement and model efficiency, with R-2 as high as 0.97. The two parameters included in the model are related to the difference between the decay constant (k factor) of tilled and no-till plots (a(2)) and also to the amount of labile carbon added to the readily decomposable soil organic matter due to tillage (a,). These two parameters were estimated in the model ranging from 1.27 and 2.60 (a(1)) and - 1.52 x 10(-2) and 2.2 x 10(-2) day(-1) (a(2)). The advantage is that temporal variability of tillage-induced emissions can be described by only one analytical function that includes the no-till emission plus an exponential term modulated by tillage and environmentally dependent parameters. (C) 2008 Elsevier B.V. All rights reserved.

Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimates of genetic parameters for scrotal circumference using random regression models in Nelore cattle

A total of 15,901 scrotal circumference (SC) records from 5300 Nelore bulls, ranging from 229 to 560 days of age, were used with the objective of estimating (co)variance functions for SC, using random regression models. Models included the fixed effects of contemporary group and age of dam at calving as covariable (linear and quadratic effects). To model the population mean trend, a third order Legendre polynomial on animal age was utilized. The direct additive genetic and animal permanent environmental random effects were modeled by Legendre polynomials on animal age, with orders of fit ranging from 1 to 5. Residual variances were modeled considering 1 (homogeneity of variance) or 4 age classes. Results obtained with the random regression models were compared to multi-trait analysis. (Co)variance estimates using multi-trait and random regression models were similar. The model considering a third- and fifth-order Legendre polynomials for additive genetic and animal permanent environmental effects, respectively, was the most adequate to model changes in variance of SC with age. Heritability estimates for SC ranged from 0.24 (229 days of age) to 0.47 (300 days of age), remained almost constant until 500 days of age (0.52), decreasing thereafter (0.44). In general, the genetic correlations between measures of scrotal circumference obtained from 229 to 560 days of age decreased with increasing distance between ages. For genetic evaluation scrotal circumference could be measured between 400 and 500 days of age. (C) 2010 Elsevier B.V. All rights reserved.

Random regression models to estimate genetic parameters for test-day milk yield in Brazilian Murrah buffaloes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Random regression models to estimate test-day milk yield genetic parameters Holstein cows in Southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling of average growth curve in Santa Ines sheep using random regression models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stochastic dynamics in exotic and native blowflies: an analysis combining laboratory experiments and a two-patch metapopulation model

In this study we explored the stochastic population dynamics of three exotic blowfly species, Chrysomya albiceps, Chrysomya megacephala and Chrysomya putoria, and two native species, Cochliomyia macellaria and Lucilia eximia, by combining a density-dependent growth model with a two-patch metapopulation model. Stochastic fecundity, survival and migration were investigated by permitting random variations between predetermined demographic boundary values based on experimental data. Lucilia eximia and Chrysomya albiceps were the species most susceptible to the risk of local extinction. Cochliomyia macellaria, C. megacephala and C. putoria exhibited lower risks of extinction when compared to the other species. The simultaneous analysis of stochastic fecundity and survival revealed an increase in the extinction risk for all species. When stochastic fecundity, survival and migration were simulated together, the coupled populations were synchronized in the five species. These results are discussed, emphasizing biological invasion and interspecific interaction dynamics.

A model for the plankton system of the Broa reservoir, Sao Carlos, Brazil

In this paper a model, called ELLOBO running in STELLA II, was set to describe the plankton system of the Broa reservoir (SP). The three state variables of the model are: phytoplankton, zooplankton, and the fish Astyanax fasciatus. The forcing variables are: temperature, nitrate, phosphorus and solar radiation. The model did not consider the cycling of nutrients inside the reservoir. The results show that: temperature is the principal forcing variable in the phytoplankton dynamic and in the subsequent evolution of the whole system. The zooplankton predation was described by Odum's equation, and there is a strong random component in zooplankton grazing, which was essential for the model, because zooplankton estimates have high variance. One must collect data in a short space of time (maybe daily) to better explain the zooplankton and phytoplankton variation. Validation was performed using simple statistics (arithmetic mean, standard deviation) and the results show concordance between observed and simulated values. Overhead was used to calibrate some parameters and to validate the model. The highest overhead value (5%) imply in the better accordance between estimated and;observed state variables values. We believe this approach in Broa reservoir will provide an useful tool for future research and it could be used comparatively in other continental aquatic ecosystems. (C) 2000 Elsevier B.V. B.V. All rights reserved.

Exploring community assembly through an individual-based model for trophic interactions

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)