.png){kind=icon}
.png){kind=icon}
Return
Graphical Copula GARCH Modeling with Dynamic Conditional Dependence
.png){kind=icon}
OA
AI
DOI：10.1080/07350015.2026.2654714.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Modeling returns on large portfolios is a challenging problem since the number of parameters in the covariance matrix grows quadratically as the size of the portfolio increases. In this article, we aim to develop a framework to model the nonlinear dependencies dynamically, namely the graphical copula GARCH (GC-GARCH) model. Motivated by the capital asset pricing model, one component of our model is independence among stock returns given some risk factors; this can greatly reduce the number of parameters, allowing the modeling of large portfolios. The joint distribution of the risk factors is factorized using a directed acyclic graph (DAG) with a pair-copula construction (PCC) to enhance the modeling of the tails of the return distribution while capturing complex dependent structures. The DAG induces topological orders to the risk factors which can be regarded as a list of directions of the flow of information. Dynamic conditional dependence structures are incorporated to allow the parameters in the copulas to be time varying. A three-stage estimation is used to estimate parameters in the marginal distributions, the risk factor copulas, and the stock copulas. The simulation study shows that the proposed estimation procedure effectively estimates the parameters and the underlying DAG structure with high accuracy. In the investment experiment presented in the empirical study, we show that the GC-GARCH model produces portfolios that, on average, yield higher returns, lower standard deviations, reduced turnover rates—indicating lower transaction costs—and greater diversification when compared to two competing copula-based models in the literature.
Keywords:
Bayesian networks
Capital asset pricing model
Dimension reduction
Pair-copula construction
Portfolio selection
Journal
J
IF:
2.5
Papers:
79
Citations:
0