Modeling Multivariate Positive-Valued Time Series Using R-INLA

Chiranjit Dutta; Nalini Ravishanker; Sumanta Basu

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arXiv · 2206.05374

Modeling Multivariate Positive-Valued Time Series Using R-INLA

Chiranjit Dutta·Nalini Ravishanker·Sumanta Basu

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Abstract

In this paper we describe fast Bayesian statistical analysis of vector positive-valued time series, with application to interesting financial data streams. We discuss a flexible level correlated model (LCM) framework for building hierarchical models for vector positive-valued time series. The LCM allows us to combine marginal gamma distributions for the positive-valued component responses, while accounting for association among the components at a latent level. We use integrated nested Laplace approximation (INLA) for fast approximate Bayesian modeling via the R-INLA package, building custom functions to handle this setup. We use the proposed method to model interdependencies between realized volatility measures from several stock indexes.

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BibTeX RIS

Chiranjit Dutta, Nalini Ravishanker, Sumanta Basu. 2022-06-10. Modeling Multivariate Positive-Valued Time Series Using R-INLA. https://arxiv.org/abs/2206.05374

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