Agradecimentos: The second author acknowledges the financial support of FAPESP (grant 2018/04654-9) and both authors acknowledge the support of the Center for Applied Research in Econometrics, Finance, and Statistics (CAREFS). This research was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant number 2018/04654-9 Ver menos

Abstract: In this paper, we propose a new method for estimating and forecasting asymmetric stochastic volatility models. The proposal is based on dynamic linear models with Markov switching written as state space models. Then, the likelihood is calculated through Kalman filter outputs and the estimates are obtained by the maximum likelihood method. Monte Carlo experiments are performed to assess the quality of estimation. In addition, a backtesting exercise with the real-life time series illustrates that the proposed method is a quick and accurate alternative for forecasting value-at-risk Ver menos