RESUMOA volatilidade é uma medida de variabilidade de uma variável que precisa ser estimada. Os diversos modelos empregados para esse fim evoluíram de estimadores simples como o desvio padrão para modelos mais sofisticados, como os modelos da família GARCH. Com o intuito de analisar as características metodológicas, evidências empíricas e principais constatações acerca dos estudos que abordaram este tema analisaram-se os artigos publicados, entre 2000 e 2014, nos principais periódicos brasileiros segundo a classificação QUALIS-CAPES 2014. Dentre os diversos resultados alcançados, evidencia-se o maior emprego do enfoque estatístico para estimar a volatilidade, o melhor desempenho dos modelos da família GARCH quando o objetivo foi comparar metodologias e a insuficiente utilização de testes diagnósticos e critérios de decisão para a validação e escolha dos melhores modelos respectivamente.
PALAVRAS-CHAVERevisão da Literatura. Volatilidade.
This study aims to evaluate how the after-market and pre-opening periods affect the estimation of conditional volatility one day ahead. Volatility features quite a lot in Finance studies because it is a fundamental parameter in derivatives pricing, the efficient allocation of portfolios, and risk management. The results are relevant for investment agents to be able to refine volatility forecasting models and achieve better results in derivatives pricing, risk management, and portfolio optimization. We used the asymmetric power autoregressive conditional heteroscedasticity (APARCH) model, incorporating the after-market, pre-opening, and total overnight periods to assess whether they contain important information for modeling volatility. We analyzed the 20 stocks of Brazilian companies listed on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBovespa) and also belonging to the BR Titans 20 with ADRs listed on the New York Stock Exchange and the Nasdaq. The results were evaluated in-sample using the corrected Akaike information criterion (AICc) and the statistical significance of the coefficients, and out-of-sample using root mean squared error (RMSE), mean absolut percentage error (MAPE), the R² of the Mincer-Zarnowitz regression, and the Diebold Mariano test. The analysis does not enable it to be claimed which is the best model, because there is no unanimity among all the stocks; however, non-regular trading hours were shown to incorporate important information for most of the stocks. Furthermore, the models that incorporated the pre-opening period generally obtained superior results to the models that incorporated the after-market period, demonstrating that this period contains important information for forecasting conditional volatility.
Brazilian agribusiness has stood out in recent years for its efficiency and productivity growth, based on technology, planning, management of results, and continuous improvement of performance. In the live cattle market, the price oscillations show themselves as a risk that the producer has to minimize in order to ensure the success of his business. In this scenario, the futures market has been translated into an important hedging instrument, however, a confronting challenge is the identification of the production ratio that must be protected. Thus, this article aims to statistically compare the performance of six models for the calculation of the optimal hedge ratio in the Brazilian live cattle futures market: Ordinary least squares, BEKK, DCC by Tse and Tsui (2002), DCC by Engle and Sheppard (2001), time-varying beta correlations, and unconditional beta. The ratios were estimated for the log-returns of the daily and monthly price series of spot and futures live cattle, comprising the period from 10/2/2000 to 19/8/2014. It was noted that for the daily series, the contractual changes generate intertemporal breaks, resulting in the increased variance of the futures logreturns and the low optimal hedge ratio. For monthly series, it is concluded that the BEKK, followed by the unconditional beta are the best models when it comes to reduction of variance and maximization of the Sharpe ratio.
Na medida em que o endividamento público alcança patamares recordes em 2021 e que crises econômicas ampliam a necessidade dos entes públicos pela tomada de crédito, o risco de crédito ganha destaque como métrica útil a potenciais credores e também aos próprios entes em termos de gerenciamento de sua carteira de passivos. Este trabalho avalia, visando ao curto prazo, as perspectivas para o risco de crédito estadual, com enfoque na probabilidade de default. Os métodos utilizados combinam projeções resultantes da aplicação de modelagem para dados em painel com simulações de Monte Carlo, tomando-se como referência conceitos atinentes à sustentabilidade de entes públicos para apuração do risco de inadimplência. Os resultados indicam a manutenção de uma condição negativa para grande parte dos estados e, também, a possibilidade de agrupamento dos entes em diferentes patamares de risco de crédito. Complementarmente, obteve-se uma ferramenta prática, alternativa e complementar para análise do risco de crédito subnacional no curto prazo.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.