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Modeling Multivariate Positive-Valued Time Series Using R-INLA

Modeling Multivariate Positive-Valued Time Series Using R-INLA

[Submitted on 10 Jun 2022 (v1), last revised 2 Jul 2022 (this version, v2)] Download PDF 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…

[Submitted on 10 Jun 2022 (v1), last revised 2 Jul 2022 (this version, v2)]

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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.

Submission history

From: Chiranjit Dutta [view email]



[v1]
Fri, 10 Jun 2022 23:15:32 UTC (46 KB)

[v2]
Sat, 2 Jul 2022 09:24:14 UTC (65 KB)

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