Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student’s-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis

Hitchhiking meloid larva upon male Eulaema mocsaryi (Hymenoptera: Apidae): a new host cleptoparasite interaction in the Amazon rainforest

The Amazon rainforest is one of the planet’s biodiversity hotspots, hosting a rich orchid bee fauna. The phoretic cleptoparasites of this bee fauna are largely unknown. We report for the first time the host–cleptoparasite interaction between Eulaema mocsaryi (Friese) (Hymenoptera: Apidae: Euglossini) and the first instar larva (triungulin) of a Tetraonycini meloid beetle. We review the host–cleptoparasite interactions of Tetraonycini with Apid bees in South America and discuss the ecological needs of the cleptoparasite. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group