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
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