Aspectos estratégicos e econômicos da política de bonificação em quantidade de produto

ste trabalho objetiva identificar a percepção dos gestores de controladoria de indústrias sobre os aspectos estratégicos e econômicos da decisão de bonificação em quantidade de produto. A premissa é que os resultados econômicos gerados pela decisão de desconto no preço e de bonificação em quantidade de produto são iguais, considerando a mesma quantidade de produto entregue ao cliente. Com base no banco de dados da FIPECAFI- FEA-USP, analisaram-se 91 questionários encaminhados a controllers atuantes em diversos setores. Os resultados revelam: 1) um número muito significativo de empresas da amostra selecionada adota a bonificação em produtos; 2) a área comercial é a principal responsável por essa decisão; e 3) identificação da percepção dos controllers sobre aspectos decisórios de bonificação em quantidade de produtos.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.

Pricing corporate bonds in Brazil: 2000 to 2004

This paper analyzes the factors that influence the issuing price of debentures in Brazil in the period from year 2000 to 2004, applying a factor model, in which exogenous variables explain return and price behavior. The variables in this study include: rating, choice of index, maturity, country risk, basic interest rate, long-term and short-term rate spread, the stock market index, and the foreign exchange rate. Results indicate that the index variable, probability of default and bond`s maturity influence pricing and points out associations of long-term bonds with better rating issues. (C) 2008 Elsevier Inc. All rights reserved.

FOOD, FUEL, FORESTS, AND THE PRICING OF ECOSYSTEM SERVICES

U.S. Department of Energy (DOE), Office of Science[DE-FG02-94ER61937]