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In this paper, a method is introduced for approximating the likelihood for the unknown parameters of a state space model. The approximation converges to the true likelihood as the simulation size goes to infinity. In addition, the approximating likelihood is continuous as a function of the...
Persistent link: https://www.econbiz.de/10010574072
In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...
Persistent link: https://www.econbiz.de/10014185810
Persistent link: https://www.econbiz.de/10009382185
In this paper we provide a unifed methodology in order to conduct likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...
Persistent link: https://www.econbiz.de/10003866080