Stochastic Volatility Models with Skewness Selection.

This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

Soybean injury caused by the application of subdoses of 2,4-D or dicamba, in simulated drift.

2,4-D or dicamba can cause injuries and other deleterious effects on non-tolerant soybeans. Thus, the objective was to evaluate the potential for injury of subdoses of 2,4-D or dicamba, in drift simulation, for application in non-tolerant soybeans. Two experiments were carried out, one with 2,4-D and the other with dicamba. The treatments consisted of the application, in post-emergence of non-tolerant soybean, of subdoses 0; 1.35; 2.68; 5.37; 10.72; 21.45 and 42.9 g acid equivalent (ae) ha-1 2,4-D choline salt or dicamba diglycolamine (DGA) salt. Injury symptoms in plants, plant height and yield were evaluated, and the results were subjected to regression analysis. Polynomial fit was possible for the doses of both herbicides, with deleterious effects on soybean, with reductions in height and yield. The application of 2,4-D ≥ 10.72 g ae ha-1 was enough to cause injuries greater than 10% in plants, in simulated drift. The application of dicamba ≥1.35 g ae ha-1 was enough to cause injuries greater than 30% in plants, in simulated drift. For both herbicides, greater potential for injury and reductions in soybean yield were observed for the application of the highest doses (21.45 and 42.9 g ae ha-1).

Modeling oil price uncertainty effects on economic growth in Mexico: a sector-level analysis.

This paper analyzes the impact of international oil price uncertainty on the different economic sectors (primary, secondary, and tertiary) in Mexico in the period 1993:1-2020:4 through a bivariate structural vector autoregressive (VAR) model with a generalized autoregressive conditional heteroskedasticity (GARCH) in mean to capture the impact of oil volatility on economic growth at the sectoral level of economic activity. The results show that the uncertainty of the international price of oil has a differentiated effect on the different sectors of economic activity in Mexico since it does not influence the primary sector; it negatively impacts the secondary sector, and there is mixed evidence in the tertiary sector. Additionally, evidence is provided that both positive and negative shocks to the international oil price have asymmetric effects at the sectoral level in Mexico. The results highlight the need to implement public policies, at the country level, that help mitigate the effect of uncertainty in the oil market and promote economic stability at the sector level.

Sectoral connectedness: New evidence from US stock market during COVID-19 pandemics.

We examine volatility connectedness of 11 sectoral indices in the US using daily data from January 01, 2013 to December 31, 2020. We employ the connectedness measures of Diebold and Yilmaz (2009, 2012, 2014), unveiling changes in sectoral connectedness and stylized facts regarding specific sectors during the COVID-19 pandemic. Among several results, we find extraordinary increase in total connectedness, from early stages of international spread to the end of July 2020; some relevant changes in the pairwise connections between sectors, especially among the originally stronger ones. However, in a total net connectedness perspective, there is little evidence of structural changes.

Measuring uncertainty: A streamlined application for the Ecuadorian economy.

This paper develops a macroeconomic uncertainty index based on the multistage procedure that combines maximum likelihood and Bayesian estimation methods proposed by Jurado et al. (Am Econ Rev 105(3):1177-1216, 2015). Our approach streamlines the computation of the macroeconomic uncertainty index by specifying a state-space model estimated by maximum likelihood that allows us to obtain in one step the parameters of the model, the dynamic factors, and the forecast errors of the macroeconomic variables used to construct the index. Moreover, we estimate stochastic volatility models on the forecast errors also by maximum likelihood using a density filter that proves to be faster than a Bayesian estimation. After showing that our methodology produces reasonable results for the USA, we apply it to compute a macroeconomic uncertainty index for Ecuador, becoming the first index of this kind for a small developing or middle-income country. The results show that the Ecuadorian economy is more volatile and less predictable during recessions. We also provide evidence that macroeconomic uncertainty is detrimental to economic activity, finding that the responses of non-oil output, employment in the formal sector, and consumer prices to macroeconomic uncertainty shocks are sizable and persistent. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00181-021-02069-5.

Exchange rate volatility, corruption, and economic growth.

This study assesses the impact of exchange rate volatility on economic growth using a panel of 194 countries for the period 1995-2019. We resort to dynamic panel data models considering the exchange rate volatility estimated based on GARCH models as an explanatory variable, along with some control variables such as the level of economic openness and financial development, investment, government spending, and the expected level of education. Countries are grouped according to the level of corruption of the governments. The estimates from both Difference and System Generalized Method of Moments are obtained. The results consistently show a significant negative effect of exchange rate volatility on economic growth, which diminishes as the financial system develops. An important finding is that the effect of volatility is lower in high-corruption countries, which could be because they are used to dealing with the economic instability associated with low levels of governance and incorporate it as part of their costs.

Stock market volatility and the COVID-19 reproductive number.

The media has prominently featured the totemic reproductive number R in its COVID-19 coverage despite being an imperfect measure of the degree of infectivity of the virus. As such, it conveys information to the public regarding the state of the pandemic that affects market sentiment. We analyze how news about R affects the volatility in stock markets worldwide and find that when R is greater than one, which means the spread of the disease should soar, it has a positive and significant effect on volatility. Our results hold after controlling for government interventions and several robustness checks.

Buffer zones for 2, 4-D applications nearby tobacco fields / Zonas de exclusão para aplicações de 2, 4-D próximas a campos de tabaco

An increase in 2,4-D use is expected as tolerant crops have been approved to use in Brazil, which may negatively affect important crops such as tobacco. Our objective was to determine safe distances between 2,4-D applications and tobacco fields considering herbicide contamination to the harvested product. A field experiment was conducted, consisting of a 2,4-D application done perpendicularly to the wind direction, using a tractor sprayer. Drifted herbicide was collected using tobacco plants placed at various points (-50 up to 400 meters from application zone), following three schemes: a) 0 to 0.5 hours after application (HAT); b) 0 to 24 HAT; and c) 0.5 to 24 HAT. Environmental conditions were recorded. Herbicide in tobacco leaves was quantified. Drift was detected up to 200 m in both years. Vapor movement of 2,4-D was detected up to 400 m from the application strip in 2016, on plants taken to the field after herbicide application. Environmental conditions in 2015 favored off-target movement (higher wind speed and air temperature and lower humidity); although, in 2016 the herbicide traveled further due to wet deposition. These results indicated that a 100-meter buffer zone is enough to significantly decrease chances of tobacco contamination above the tolerated threshold, and highlighted the importance of environmental conditions in the transport processes for 2,4-D under field conditions.

O aumento no uso do 2,4-D é esperado, uma vez que as culturas tolerantes foram aprovadas para uso no Brasil, o que pode afetar negativamente culturas importantes, como o tabaco. Nosso objetivo foi determinar a distância segura entre aplicações de 2,4-D e lavouras de tabaco, considerando a contaminação do herbicida no produto colhido. Um experimento de campo foi realizado, utilizando aplicação de 2,4-D perpendicularmente à direção do vento, com pulverizador tratorizado. A deriva do herbicida foi coletada plantas de tabaco colocadas em vários pontos (-50 até 400 metros da zona de aplicação), seguindo três esquemas: a) 0 a 0,5 horas após a aplicação (HAT); b) 0 a 24 HAT; e c) 0,5 a 24 HAT. As condições ambientais foram registradas. Foi quantificado o herbicida em folhas de tabaco. A deriva foi detectada até 200 m em 2015 e até 150 m em 2016. A volatilização ocorreu em ambos os anos, pois o 2,4-D foi detectado em plantas transportadas para o campo após a aplicação. As condições ambientais em 2015 favoreceram o movimento fora do alvo (maior velocidade do vento e temperatura do ar e menor umidade). Esses resultados indicam que uma zona de exclusão de 100 metros é suficiente para diminuir significativamente as chances de contaminação do tabaco acima do limite tolerado e, destaca a importância das condições ambientais nos processos de transporte do 2,4-D em condições de campo.

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.

Does digitalization matter in green preferences in nexus of output volatility and environmental quality?

The fact is that output volatility and carbon dioxide (CO2) emissions move together over the period. This empirical study examines the dynamic effect of output volatility on CO2 emissions using the advance nonlinear panel autoregressive distributed lag (ARDL) approach. The empirical analysis is executed for ten high emitters Asian countries covering the period from 1990 to 2019. The findings reveal that positive change in output volatility increases CO2 emissions and negative change in output volatility decreases CO2 emissions in the long run in Asia. The results also show that digitization also positively impacts environmental quality in Asia due to green globalization. The findings are also robust and similar in an alternative indicator of the environment. An important policy is that reducing volatility in output is a suitable way of environmental sustainability, particularly for Asian countries.

Google Health Trends performance reflecting dengue incidence for the Brazilian states.

BACKGROUND: Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags. METHODS: We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective. RESULTS: From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index. CONCLUSIONS: The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.

Persistence effect determination of variability in forecasting of agricultural and road machinery national production / Determinação do efeito de persistência da variabilidade na previsão da produção nacional de máquinas agrícolas e rodoviárias

The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.(AU)

O objetivo desta pesquisa é prever a produção nacional de máquinas agrícolas e rodoviárias no Brasil, no curto prazo por meio da metodologia BOX & JENKINS e determinar o efeito de persistência na série. Os dados foram obtidos no site da Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) no período de janeiro de 1960 a outubro de 2019, totalizando 718 observações mensais. Os modelos Autoregressivos Integrados e de Médias Móveis (ARIMA) e de Heteroscedasticidade Condicional Autoregressiva (ARCH) foram utilizados para ajustar a média e a variabilidade da série. O modelo ARIMA(2,1,1) - ARCH(2) foi selecionado por meio das estatísticas de ajustes e a persistência determinada foi de 0,60 mostrando que a instabilidade na série é duradoura.(AU)

Persistence effect determination of variability in forecasting of agricultural and road machinery national production / Determinação do efeito de persistência da variabilidade na previsão da produção nacional de máquinas agrícolas e rodoviárias

ABSTRACT: The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.

RESUMO: O objetivo desta pesquisa é prever a produção nacional de máquinas agrícolas e rodoviárias no Brasil, no curto prazo por meio da metodologia BOX & JENKINS e determinar o efeito de persistência na série. Os dados foram obtidos no site da Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) no período de janeiro de 1960 a outubro de 2019, totalizando 718 observações mensais. Os modelos Autoregressivos Integrados e de Médias Móveis (ARIMA) e de Heteroscedasticidade Condicional Autoregressiva (ARCH) foram utilizados para ajustar a média e a variabilidade da série. O modelo ARIMA(2,1,1) - ARCH(2) foi selecionado por meio das estatísticas de ajustes e a persistência determinada foi de 0,60 mostrando que a instabilidade na série é duradoura.

Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution.

A stochastic volatility-in-mean model with correlated errors using the generalized hyperbolic skew Student-t (GHST) distribution provides a robust alternative to the parameter estimation for daily stock returns in the absence of normality. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm is developed for parameter estimation. The deviance information, the Bayesian predictive information and the log-predictive score criterion are used to assess the fit of the proposed model. The proposed method is applied to an analysis of the daily stock return data from the Standard & Poor's 500 index (S&P 500). The empirical results reveal that the stochastic volatility-in-mean model with correlated errors and GH-ST distribution leads to a significant improvement in the goodness-of-fit for the S&P 500 index returns dataset over the usual normal model.

Volatilidade dos resultados de proficiência e seu impacto sobre as metas do ideb nas escolas públicas de minas gerais / Volatility of proficiency results and its impact on ideb goals in the public schools of minas gerais

RESUMO: Este artigo tem dois objetivos principais: mensurar a volatilidade dos resultados das avaliações de Língua Portuguesa e Matemática nas escolas públicas mineiras; e estimar o possível impacto dessas flutuações nos resultados do cumprimento das metas do Ideb. Para tal, analisa-se um banco com dados provenientes da Prova Brasil e do Sistema Mineiro de Avaliação da Educação Básica (Simave). O método estatístico empregado é um modelo longitudinal linear hierárquico que tem a escola como nível de interesse. Entre as principais conclusões obtidas estão: o fato de que as flutuações das notas médias das escolas são bastante acentuadas, mesmo quando se leva em conta a sua tendência de crescimento; e a constatação de que o impacto sobre o cumprimento das metas do Ideb, devido a tais flutuações, pode ser considerável, a ponto de facilitar ou dificultar o sucesso das escolas quanto ao seu cumprimento.

ABSTRACT: This paper has two main purposes. The first one is to obtain a volatility measure of Portuguese and Mathematics mean scores in Minas Gerais State public schools. The second one is to estimate the probable impact of these fluctuations on results of respective Ideb goals. The analysis is based on data obtained from Prova Brasil and Simave (Sistema Mineiro de Avaliação da Educação Básica) tests. The used statistical technique is a longitudinal hierarchical model with the schools as the level of interest. One of the main conclusions obtained is the fact that fluctuation in school mean scores is considerable, even when one takes into account its growth rates. Another relevant conclusion is the fact that this fluctuation can have a considerable impact on the success or failure of a given school in reaching its respective Ideb goals.

A new logistic-type model for pricing European options.

We propose a family of models for the evolution of the price process [Formula: see text] of a financial market. We model share price and volatility using a two-dimensional system of stochastic differential equations (SDEs) driven by a single Wiener process. We prove that this family of models is well defined and that each model from this family is free of arbitrage opportunities, and it is (state) complete. We use option prices written over the S&P500 from December 2007 to December 2008 to calibrate a model of the proposed family and compare the calibration results with results of the Heston Model for the same data set. The empirical results achieved in both models show similarities for periods of low volatility, but the model studied shows a better performance during the period of higher volatility.

Estimación biométrica del volumen de humor acuoso en ojos con aceite de silicona en medios opacos / Biometric estimation aqueous volume in eyes with silicone oil in opaque media

Propósito: Estimar el volumen de humor acuoso como índice de viabilidad ocular. Métodos: El método de cálculo integral de sólidos en revolución por discos, es el utilizado para calcular el volumen del humor acuoso con forma de menisco depositado debajo del aceite de silicona gracias al efecto de la gravedad, y basando dichos cálculos en la mediciones biométricas de diferentes cortes ecográficos, que abarquen tanto el aceite de silicona como el humor acuoso, mediante ecografía ocular. Resultados: De tres modelos matemáticos realizados para el cálculo del volumen del menisco, uno de ellos es el que más se acerca a los valores empíricos, mostrando menor volatilidad en los resultados.

Purpose: To estimate the volume of aqueous humor as an ocular viability index. Methods: The integral calculus of solids in revolution by disks is the method used to calculate the volume of aqueous shaped meniscus deposited under the silicone oil through the effect of gravity, and basing such calculations on biometric measurements of different ultrasound sections, covering both silicone oil as the aqueous humor, by using ocular ultrasound. Results: From the three mathematical models made for calculating the volume of the meniscus, one of them is the closest to the empirical values, showing less volatility in results.

Uso de modelos bayesianos de volatilidade estocástica no estudo temporal dos registros de doações de sangue. / Using Bayesian stochastic volatility models in a temporal study of the records of blood donors.

Na área das finanças, modelos como o ARCH (autoregressive conditional heteroscedaticity), GARCH (general autoregressive conditional heteroscedasticity) e o modelo de volatilidade estocástica (MVE) são amplamente utilizados na análise de séries de tempo. Por outro lado, essas ferramentas são pouco difundidas na área da saúde. No presente estudo, buscamos transportar os conceitos do MVE para a análise dos registros de doações de sangue do Hemocentro de Ribeirão Preto, São Paulo, realizadas no período de julho de 1996 a junho de 2005. Para isso, utilizamos uma modelagem bayesiana baseada em métodos Monte Carlo em cadeia de Markov. Esse modelo é capaz de apontar os períodos de maior alteração do fluxo de doadores de sangue captados na rotina mensal do Hemocentro ao longo dos anos, e os seus resultados são de grande utilidade para o planejamento de campanhas de doação e captação de doadores, quando identificados os períodos mais críticos para os estoques de bolsas de sangue. O MVE evidencia que, nos anos que compõem o período estudado, o número de doações é caracterizado por uma grande diminuição no número de doações em dezembro, um aumento posterior no mês de janeiro e novamente uma queda em fevereiro.

In studies from the financial literature, models as ARCH (autoregressive conditional heteroscedaticity), GARCH (general autoregressive conditional heteroscedasticity) and the volatility stochastic model are extensively used in the analysis of time series. However, the application of these tools in the health is inexpressive. In the present study, we aimed to adapt the concepts from the volatility stochastic model to the analysis of the records of blood donors who attend Ribeirão Preto Blood Center, São Paulo, Brazil, between July 1996 and June 2005. For this purpose, we used a Bayesian approach based in Markov chain Monte Carlo (MCMC) methods. This model can identify the periods of time when the flux of blood donations collected in the mensal routine of the Blood Center is subject to the largest variation over the years, and its results are very useful for the planning of donation campaigns and search for blood donors, when the most critical periods of blood supply are identified. The volatility stochastic model shows that in the studied period, there is a large decrease in the number of blood donations in December, a subsequent increase in January, and a new decrease in February.