Threshold network GARCH model

Pan, Yue and Pan, Jiazhu (2024) Threshold network GARCH model. Journal of Time Series Analysis. ISSN 0143-9782 (https://doi.org/10.1111/jtsa.12743)

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Abstract

Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐type model that can simplify the parameterization by utilizing the network structure that can be appropriately specified for certain types of high‐dimensional data. The asymmetry in the dynamics of volatilities is also considered as our model adopts a threshold structure. To enable our model to handle data with extremely high dimension, we investigate the near‐epoch dependence (NED) of our model, and the asymptotic properties of our quasi‐maximum‐likelihood‐estimator (QMLE) are derived from the limit theorems for NED random fields. Simulations are conducted to test our theoretical results. At last we fit our model to log‐returns of four groups of stocks and the results indicate that bad news is not necessarily more influential on volatility if the network effects are considered.

  • Item type:Article
    ID code:89104
    Dates:
    Date
    Event
    13 May 2024
    Published
    13 May 2024
    Published Online
    11 April 2024
    Accepted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:02 May 2024 15:39
    Last modified:07 Jun 2024 01:49
    URI:https://strathprints.strath.ac.uk/id/eprint/89104
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