Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension
This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burde...
Main Authors: | João Pedro Coli de Souza Monteneri Nacinben, Márcio Laurini |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Econometrics |
Subjects: | |
Online Access: | https://www.mdpi.com/2225-1146/12/1/5 |
Similar Items
-
Multivariate normal-Laplace distribution and processes
by: Kanichukattu Korakutty Jose, et al.
Published: (2014-12-01) -
Bayesian Inference for Long Memory Stochastic Volatility Models
by: Pedro Chaim, et al.
Published: (2024-11-01) -
Multivariate Escher Transformed Laplace Distribution and Its Generalization
by: H Rimsha, et al.
Published: (2020-07-01) -
Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia
by: Melkamu Dedefo, et al.
Published: (2018-11-01) -
“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study
by: Yuliya Shapovalova
Published: (2021-04-01)