A Bayesian semiparametric vector Multiplicative Error Model

Donelli, Nicola; Peluso, Stefano; Mira, Antonietta

doi:10.1016/j.csda.2021.107242

Cited by 2 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since then the literature has expanded significantly from the realized volatility of Andersen and Bollerslev (1998) to bipower variation (Barndorff-Nielsen and Shephard, 2004), realized kernels of Barndorff-Nielsen et al (2009) and realized covariance matrices (Aït-Sahalia et al, 2010, Peluso et al, 2014. As these intraday volatility measures evolved, there has been a natural complementary effort to build adequate models to describe their joint dynamics , Donelli et al, 2021, Cipollini et al, 2013.…”

Section: Modeling Multivariate Realized Volatility Measuresmentioning

confidence: 99%

Modeling Multivariate Positive-Valued Time Series Using R-INLA

Dutta¹,

Равишанкер²,

Basu³

2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Modeling Multivariate Realized Volatility Measuresmentioning

confidence: 99%

Modeling Multivariate Positive-Valued Time Series Using R-INLA

Dutta¹,

Равишанкер²,

Basu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

Modeling multivariate positive‐valued time series using R‐INLA

Dutta,

Ravishanker,

Basu

2024

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

In this article, 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 introduce vector autoregression evolution of the latent states, deriving its precision matrix and enabling its estimation using 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 intraday volatility measures from several stock indexes.

show abstract

A Bayesian semiparametric vector Multiplicative Error Model

Cited by 2 publications

References 31 publications

Modeling Multivariate Positive-Valued Time Series Using R-INLA

Modeling Multivariate Positive-Valued Time Series Using R-INLA

Modeling multivariate positive‐valued time series using R‐INLA

Contact Info

Product

Resources

About