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Modelling stock data using a geometric BINAR(1) model
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The intricate cross-correlation between the counting series makes the non-stationary bivariate integer-valued autoregressive of order 1 (BINAR(1)) model with a full correlation structure very challenging to model. This paper offers a novel full correlation nonstationary BINAR(1) model with geometric marginals (BINAR(1)GEOM). The regression and dependency parameters are estimated using the Generalized Quasi-Likelihood (GQL) estimation approach, which is contrasted with several well-known estimation techniques: the Generalized Method of Moments (GMM) and Generalized Least Squares (GLS). A numerical experiment is conducted to compare the performance of these estimation methods and the proposed model is tested on some stock transactions in the Mauritian market.
Taru Publications
Title: Modelling stock data using a geometric BINAR(1) model
Description:
The intricate cross-correlation between the counting series makes the non-stationary bivariate integer-valued autoregressive of order 1 (BINAR(1)) model with a full correlation structure very challenging to model.
This paper offers a novel full correlation nonstationary BINAR(1) model with geometric marginals (BINAR(1)GEOM).
The regression and dependency parameters are estimated using the Generalized Quasi-Likelihood (GQL) estimation approach, which is contrasted with several well-known estimation techniques: the Generalized Method of Moments (GMM) and Generalized Least Squares (GLS).
A numerical experiment is conducted to compare the performance of these estimation methods and the proposed model is tested on some stock transactions in the Mauritian market.
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