Bayesian Inference for Long Memory Stochastic Volatility Models

Bayesian Inference for Long Memory Stochastic Volatility Models

We explore the application of integrated nested Laplace approximations for the Bayesian estimation of stochastic volatility models characterized by long memory. The logarithmic variance persistence in these models is represented by a Fractional Gaussian Noise process, which we approximate as a linea...

Full description

Saved in:
Bibliographic Details
Main Authors: Pedro Chaim, Márcio Poletti Laurini
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Econometrics
Subjects:
long memory
Gaussian Markov random fields
Laplace approximations
volatility forecasting
Online Access:https://www.mdpi.com/2225-1146/12/4/35
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2225-1146/12/4/35

Similar Items