A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference

Wei Ting Lai; Ray Bing Chen; Shih Feng Huang

doi:10.1016/j.ijforecast.2024.06.002

A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference

Wei Ting Lai, Ray Bing Chen, Shih Feng Huang

Graduate Institute of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.

Original language	English
Pages (from-to)	345-360
Number of pages	16
Journal	International Journal of Forecasting
Volume	41
Issue number	1
DOIs	https://doi.org/10.1016/j.ijforecast.2024.06.002
State	Published - 1 Jan 2025

Keywords

Asynchronous time series
GARCH
Variable selection
Variational Bayesian inference
Vector autoregressive model

Access to Document

10.1016/j.ijforecast.2024.06.002

Cite this

@article{5c0690bec08f4d4484938bb55f7b879d,

title = "A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference",

abstract = "This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.",

keywords = "Asynchronous time series, GARCH, Variable selection, Variational Bayesian inference, Vector autoregressive model",

author = "Lai, {Wei Ting} and Chen, {Ray Bing} and Huang, {Shih Feng}",

note = "Publisher Copyright: {\textcopyright} 2024 International Institute of Forecasters",

year = "2025",

month = jan,

day = "1",

doi = "10.1016/j.ijforecast.2024.06.002",

language = "???core.languages.en_GB???",

volume = "41",

pages = "345--360",

journal = "International Journal of Forecasting",

issn = "0169-2070",

number = "1",

}

TY - JOUR

T1 - A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference

AU - Lai, Wei Ting

AU - Chen, Ray Bing

AU - Huang, Shih Feng

PY - 2025/1/1

Y1 - 2025/1/1

N2 - This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.

AB - This study proposes a modified VAR-deGARCH model, denoted by M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed for the M-VAR-deGARCH model to infer structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performance of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performance. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.

KW - Asynchronous time series

KW - GARCH

KW - Variable selection

KW - Variational Bayesian inference

KW - Vector autoregressive model

UR - http://www.scopus.com/inward/record.url?scp=85196645510&partnerID=8YFLogxK

U2 - 10.1016/j.ijforecast.2024.06.002

DO - 10.1016/j.ijforecast.2024.06.002

M3 - 期刊論文

AN - SCOPUS:85196645510

SN - 0169-2070

VL - 41

SP - 345

EP - 360

JO - International Journal of Forecasting

JF - International Journal of Forecasting

IS - 1

ER -

A modified VAR-deGARCH model for asynchronous multivariate financial time series via variational Bayesian inference

Abstract

Keywords

Access to Document

Fingerprint

Cite this