In this paper we consider mixture generalized autoregressive conditional heteroskedastic models, and propose a new iteration algorithm of type EM for the estimation of model parameters. The maximum likelihood estimates are shown to be consistent, and their asymptotic properties are investigated. More precisely, we derive simple expressions in closed form for the asymptotic covariance matrix and the expected Fisher information matrix of the ML estimator. Finally, we study the model selection and propose testing procedures. A simulation study and an application to financial real series illustrate the results.

Statistical Inference for Mixture GARCH Models with Financial Application / Cavicchioli, Maddalena. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 36:(2021), pp. 2615-2642. [10.1007/s00180-021-01092-5]

Statistical Inference for Mixture GARCH Models with Financial Application

Cavicchioli Maddalena
2021

Abstract

In this paper we consider mixture generalized autoregressive conditional heteroskedastic models, and propose a new iteration algorithm of type EM for the estimation of model parameters. The maximum likelihood estimates are shown to be consistent, and their asymptotic properties are investigated. More precisely, we derive simple expressions in closed form for the asymptotic covariance matrix and the expected Fisher information matrix of the ML estimator. Finally, we study the model selection and propose testing procedures. A simulation study and an application to financial real series illustrate the results.
2021
36
2615
2642
Statistical Inference for Mixture GARCH Models with Financial Application / Cavicchioli, Maddalena. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 36:(2021), pp. 2615-2642. [10.1007/s00180-021-01092-5]
Cavicchioli, Maddalena
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1236431
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