A5056629806- Financial Risk and Volatility Modeling
- Complex Systems and Time Series Analysis
- Market Dynamics and Volatility
- Efficiency Analysis Using DEA
- Simulation Techniques and Applications
- Energy Load and Power Forecasting
- Advanced Queuing Theory Analysis
- Economics of Agriculture and Food Markets
- Insurance, Mortality, Demography, Risk Management
- Economic theories and models
- SARS-CoV-2 detection and testing
- SARS-CoV-2 and COVID-19 Research
- Financial Reporting and Valuation Research
- Viral gastroenteritis research and epidemiology
Pontifícia Universidade Católica do Rio Grande do Sul
2024
Universidade Federal do Rio Grande do Sul
2016-2024
University of Rio Grande and Rio Grande Community College
2016
Universidade Federal de Santa Catarina
2013
SARS-CoV-2 genome surveillance is important for monitoring risk groups and health workers as well data on new cases mortality rate due to COVID-19. We characterized the circulation of variants from May 2021 April 2022 in state Santa Catarina, southern Brazil, evaluated similarity between present population healthcare (HCW). A total 5291 sequenced genomes demonstrated 55 strains four concern (Alpha, Delta, Gamma Omicron—sublineages BA.1 BA.2). The number was relatively low 2021, but deaths...
O objetivo deste trabalho é identificar tendências de alta e baixa no índice Bovespa. Para tanto, são estimados modelos com mudanças regime markovianas que incorporam dependência duração, nos quais a probabilidade transição depende também do número períodos em o processo se encontra determinado estado. Os resultados mostraram um retorno positivo volatilidade, outro volatilidade negativo. Ademais, troca diminui persistência mercado baixa. Uma análise das probabilidades suavizadas evidência...
One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is duration hidden states. Because there currently no procedure for estimating this or testing whether a given appropriate data set, an ad hoc choice must be heuristically justified. In paper, we propose and examine methodology that mitigates DDMS when forecasting goal. The novelty paper use asymmetric Aranda-Ordaz parametric link function to model transition probabilities models, instead commonly...
ABSTRACT Duration‐dependent Markov‐switching (DDMS) models require a user‐specified duration hyperparameter, for which there is currently no established procedure estimation or testing. As result, an ad‐hoc choice must be heuristically justified. This paper proposes methodology handling selection in DDMS models, with focus on volatility forecasting. The main novelty lies generating forecasts through model combination techniques. idea that the combined will more robust to misspecification...
One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is duration hidden states. Because there currently no procedure for estimating this or testing whether a given appropriate data set, an ad hoc choice must be heuristically justified. In paper, we propose and examine methodology that mitigates DDMS when forecasting goal. Two Monte Carlo simulations, based on classical applications models, are employed to evaluate methodology. addition, empirical...