EconPapers    
Economics at your fingertips  
 

High‐dimensional sparse multivariate stochastic volatility models

Benjamin Poignard and Manabu Asai

Journal of Time Series Analysis, 2023, vol. 44, issue 1, 4-22

Abstract: Although multivariate stochastic volatility models usually produce more accurate forecasts compared with the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state‐space model, we carry out a two‐step penalized procedure. We provide the asymptotic properties of the two‐step estimator and the oracle property of the first‐step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data. Supplementary Material presenting technical proofs is available online.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jtsa.12647

Related works:
Working Paper: High-Dimensional Sparse Multivariate Stochastic Volatility Models (2022) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:44:y:2023:i:1:p:4-22

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-03-19
Handle: RePEc:bla:jtsera:v:44:y:2023:i:1:p:4-22