Modeling RCOV matrices with a generalized threshold conditional autoregressive Wishart model

Yan CUI, Fukang ZHU, Wai Keung LI

Research output: Contribution to journalArticle

Abstract

In this article, we propose a generalized threshold conditional autoregressive Wishart (GTCAW) model to analyze the dynamics of the realized covariance (RCOV) matrices. This model extends the idea of [29] to a threshold framework. It is believed that, as in many financial time series, the dynamic of RCOV matrices exhibits nonlinearity and may be better explained by a threshold type model. The noncentrality matrix and scale matrix of the Wishart distribution are piecewise linear driven by the lagged values of RCOV matrices and retain two different sources of dynamics. The GTCAW model guarantees the symmetry and positive definiteness of RCOV matrices, some simulation results on the maximum likelihood estimation are also given. Real data examples based on daily RCOV matrices present the nonlinear behavior in these time series and the usefulness of the proposed model. Copyright © 2020 International Press of Boston, Inc.
Original languageEnglish
Pages (from-to)77-89
JournalStatistics and its Interface
Volume13
Issue number1
DOIs
Publication statusPublished - 2020

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Covariance matrix
Modeling
Time series
Wishart Distribution
Positive Definiteness
Model
Financial Time Series
Maximum likelihood estimation
Maximum Likelihood Estimation
Piecewise Linear
Nonlinearity
Symmetry
Simulation

Citation

Cui, Y., Zhu, F., & Li, W. K. (2020). Modeling RCOV matrices with a generalized threshold conditional autoregressive Wishart model. Statistics and Its Interface, 13(1), 77-89. doi: 10.4310/SII.2020.v13.n1.a7

Keywords

  • GTCAW
  • RCOV matrices
  • Threshold
  • Volatility
  • Wishart distribution