Portfolio Efficiency Tests with Conditioning Information-Comparing GMM and GEL Estimators.

We evaluate the use of generalized empirical likelihood (GEL) estimators in portfolio efficiency tests for asset pricing models in the presence of conditional information. The use of conditional information is relevant to portfolio management as it allows for checking whether asset allocations are efficiently exploiting all the information available in the market. Estimators from the GEL family present some optimal statistical properties, such as robustness to misspecifications and better properties in finite samples. Unlike generalized method of moments (GMM) estimators, the bias for GEL estimators does not increase with the number of moment conditions included, which is expected in conditional efficiency analysis. Due to these better properties in finite samples, our main hypothesis is that portfolio efficiency tests using GEL estimators may have better properties in terms of size, power, and robustness. Using Monte Carlo experiments, we show that GEL estimators have better performance in the presence of data contaminations, especially under heavy tails and outliers. Extensive empirical analyses show the properties of the estimators for different sample sizes and portfolio types for two asset pricing models.

Spatial heterogeneity in climate change effects across Brazilian biomes.

We present a methodology designed to study the spatial heterogeneity of climate change. Our approach involves decomposing the observed changes in temperature patterns into multiple trend, cycle, and seasonal components within a spatio-temporal model. We apply this method to test the hypothesis of a global long-term temperature trend against multiple trends in distinct biomes. Applying this methodology, we delve into the examination of heterogeneity of climate change in Brazil-a country characterized by a spectrum of climate zones. The findings challenge the notion of a global trend, revealing the presence of distinct trends in warming effects, and more accelerated trends for the Amazon and Cerrado biomes, indicating a composition between global warming and deforestation in determining changes in permanent temperature patterns.

A spatio-temporal analysis of fire occurrence patterns in the Brazilian Amazon.

Wildfires in the Amazon significantly impact the forest structure and carbon cycle. Understanding the patterns of fire occurrence is crucial for effective management. A novel spatio-temporal point process framework was used to analyze changes in fire occurrence patterns in the Brazilian Amazon. A dynamic representation of a Log Gaussian Cox process was used to model the intensity function, which was decomposed into trend, seasonality, cycles, covariates, and spatial effects. The results show a marked decrease in long-term fire occurrence movements between the start of the sample and 2012, followed by an increase until the end of the sample, attributed to governance measures and market mechanisms. Spatial variability of fire occurrence rates in the Brazilian Amazon was successfully captured, with regions having more dry seasons experiencing higher fire occurrence rates. This analysis provides valuable insights into fire occurrence patterns in the Amazon region and the factors driving them.

Time-varying higher moments in Bitcoin.

Cryptocurrencies represent a new and important class of investments but are associated with asymmetric distributions and extreme price changes. We use a modeling structure where higher-order moments (scale, skewness and kurtosis) are time-varying, and additionally we used nontraditional innovations distributions to study the return series of the most important cryptocurrency, Bitcoin. Based on the estimation of a series of Generalized Autoregressive Score (GAS) models, we compare predictive performance using a loss function based on Value at Risk performance.

Brazilian stock market bubble in the 2010s.

Brazilian stock markets underwent a period of remarkable exuberance between early 2016 and March 2020, only to crash with the global turmoil related to health worries and oil prices. The Ibovespa index tripled its market value between a low point in January 2016 and its maximum in January 2020-by March 12, half those gains had been erased. Narratives about a bubble in Brazilian stocks before the global crash and its subsequent burst are plentiful in specialized media. In this paper, we explore this narrative from within the framework of strict local martingale financial bubbles. A key result in this literature states some financial asset price displays a bubble only if it follows a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We first apply a nonparametric method to estimate the volatility function of Ibovespa daily prices, then fit a stochastic volatility model whose parameter values can discriminate the underlying price process as either a true martingale or a strict local martingale. Our results are negative towards the presence of a strict local martingale bubble in the Ibovespa index. Strict local martingale bubbles are related to a positive relationship between returns and volatility which does not seem present in the data at hand. We also performed a comparative analysis of the patterns found for the Ibovespa with the S&P500 index, spot Brent oil and gold prices.

Robust trend estimation for COVID-19 in Brazil.

Estimating patterns of occurrence of cases and deaths related to the COVID-19 pandemic is a complex problem. The incidence of cases presents a great spatial and temporal heterogeneity, and the mechanisms of accounting for occurrences adopted by health departments induce a process of measurement error that alters the dependence structure of the process. In this work we propose methods to estimate the trend in the cases of COVID-19, controlling for the presence of measurement error. This decomposition is presented in Bayesian time series and spatio-temporal models for counting processes with latent components, and compared to the empirical analysis based on moving averages. We applied time series decompositions for the total number of deaths in Brazil and for the states of São Paulo and Amazonas, and a spatio-temporal analysis for all occurrences of deaths at the state level in Brazil, using two alternative specifications with global and regional components.

Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale.

OBJECTIVE: Our main objective is to estimate the trend of deaths by COVID-19 on a global scale, considering the six continents. STUDY DESIGN: The study design was a retrospective observational study conducted using the secondary data provided by the Our World in Data project on a public domain. SETTING: This study was conducted based on worldwide deaths by COVID-19 recorded for the Our World in Data project from 29 February 2020 to 17 February 2021. METHODS: Estimating the trend in COVID-19 deaths is not a trivial task due to the problems associated with the COVID-19 data, such as the spatial and temporal heterogeneity, observed seasonality and the delay between the onset of symptoms and diagnosis, indicating a relevant measurement error problem and changing the series' dependency structure. To bypass the aforementioned problems, we propose a method to estimate the components of trend, seasonality and cycle in COVID-19 data, controlling for the presence of measurement error and considering the spatial heterogeneity. We used the proposed model to estimate the trend component of deaths by COVID-19 on a global scale. RESULTS: The model was able to capture the patterns in the occurrence of deaths related to COVID-19, overcoming the problems observed in COVID-19 data. We found compelling evidence that spatiotemporal models are more accurate than univariate models to estimate the patterns of the occurrence of deaths. Based on the measures of dispersion of the models' prediction in relation to observed deaths, it is possible to note that the models with spatial component are significantly superior to the univariate model. CONCLUSION: The findings suggested that the spatial dynamics have an important role in the COVID-19 epidemic process since the results provided evidence that spatiotemporal models are more accurate to estimate the general patterns of the occurrence of deaths related to COVID-19.