Statistical Modelling 24 (1) (2024), 58–81
A time-varying GARCH mixed-effects model for isolating
high- and low-frequency volatility and co-volatility
Zeynab Aghabazaz,
Department of Statistics,
College of Science,
Shiraz University,
Iran.
Iraj Kazemi,
Department of Statistics,
Faculty of Mathematics & Statistics,
University of Isfahan,
Iran.
e-mail: i.kazemi@stat.ui.ac.ir
Alireza Nematollahi,
Department of Statistics,
College of Science,
Shiraz University,
Iran.
Abstract:
This article studies long-term, short-term volatility and co-volatility in stock markets by introducing modelling strategies to the multivariate data analysis that deal with serially correlated innovations and cross-section dependence. In particular, it presents an innovative mixed-effects model through a GARCH process, allowing for heterogeneity effects and time-series dynamics. We propose a non-parametric regression model of the penalized low-rank smoothing spline to present time trends into the variance and covariance equations. The strategy provides flexible modelling of the low-frequency volatility and co-volatility in equity markets. The decomposed low-frequency matrix was modelled using the modified Cholesky factorization. The Hamiltonian Monte Carlo technique is implemented as a Bayesian computing process for estimating parameters and latent factors. The advantage of our modelling strategy in empirical studies is highlighted by examining the effect of latent financial factors on a panel across 10 equities over 110 weekly series. The model can differentiate non-parametrically dynamic patterns of high and low frequencies of variance–covariance structural equations and incorporate economic features to predict variabilities in stock markets regarding time-series evidence.
Keywords:
Hamiltonian Monte Carlo,
Modified Cholesky decomposition,
multivariate GARCH,
non-parametric regression,
optimization
Downloads:
Data and code in tar.gz archive.
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