The Asymmetric Long Memory Stochastic Volatility (A-LMSV) model has two attractive features for modeling financial returns: i) the autocorrelation function of the log-variance presents hyperbolic decay, and ii) the two driven random noises that define the model have nonzero correlation. In this work we present a maximum likelihood method for estimating both the parameters and the unobserved components, together with a method for value-at-risk (VaR) forecasting. Our method takes advantage of a state space representation of the model which is written as a dynamic linear model with Markov switching. Then, the likelihood is readily calculated by the Kalman filter. The proposed method is assessed by Monte Carlo experiments and real-life illustrations.
Comissão Organizadora
Anderson Odias da Silva
Claudia Yoshinaga
Ricardo D. Brito
Felipe Saraiva Iachan
Vinicius Augusto Brunassi Silva
Complete tool for submission, evaluation and publication of proceedings for scientific events.
