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On variable ordination of modified Cholesky decomposition for estimating time‐varying covariance matrices

  • Published:2019-12-02 Issue:3 Volume:88 Page:616-641
  • ISSN:0306-7734
  • Container-title:International Statistical Review
  • language:en
  • Short-container-title:International Statistical Review

Author:

Kang Xiaoning1,Deng Xinwei2ORCID,Tsui Kam‐Wah3,Pourahmadi Mohsen4

Affiliation:

1. Institute of Supply Chain Analytics and International Business College Dongbei University of Finance and Economics Dalian China

2. Department of Statistics Virginia Tech Blacksburg Virginia

3. Department of Statistics University of Wisconsin‐Madison Madison Wisconsin

4. Department of Statistics Texas A&M University College Station Texas

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference58 articles.

1. Orthogonal GARCH;Alexander C.;Master. Risk,2001

2. Contagion determination via copula and volatility threshold models

3. Efficient bayesian estimation and combination of GARCH‐type models;Ardia D.;Rethink Risk Measure Report: Examples Appl Financ,2010

4. A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation

5. Evaluation of multivariate GARCH models in an optimal asset allocation framework

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1. Cholesky GAS models for large time-varying covariance matrices;Journal of Management Science and Engineering;2024-03

2. Two Gaussian Regularization Methods for Time-Varying Networks;Journal of Agricultural, Biological and Environmental Statistics;2024-01-02

3. Estimation of banded time-varying precision matrix based on SCAD and group lasso;Computational Statistics & Data Analysis;2024-01

4. Ensemble LDA via the modified Cholesky decomposition;Computational Statistics & Data Analysis;2023-12

5. AutoRegressive approximations to nonstationary time series with inference and applications;The Annals of Statistics;2023-06-01
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