A0203
Title: Graphical copula GARCH modeling with dynamic conditional dependence
Authors: Mike So - The Hong Kong University of Science and Technology (Hong Kong) [presenting]
Abstract: The aim is to develop a graphical copula GARCH model for volatility modeling. To allow high-dimensional modeling for large portfolios, the complexity of the modeling is greatly reduced by introducing conditional independence among stocks given the market risk factors, such as the S\&P500 index in the United States. The market risk factors are modeled using a directed acyclic graph (DAG) model with a pairwise-copula construction to allow flexible distributional modeling. The use of the DAG model gives a topological order to the market risk factors, which can be regarded as a list of directions of the flow of information or disturbance. The conditional distributions among stock returns are also modeled through pairwise-copula constructions for flexibility. We adopt dynamic conditional dependence structures to allow the parameters in the copulas to be time-varying such that we can model dynamically the tail dependence between any two stocks. Three-stage estimation is used for estimating parameters in the marginal distributions, the copulas of the DAG of the market risk factors, and the copulas of the stocks. Bayesian inference is used to learn the structure of the DAG. In the simulation study, we show that these estimation procedures can be used to recover the parameters and the DAG accurately. With Bayesian inference, we can allow the structure of the market risk factors to be random, and model averaging can be done to obtain robust predictions of volatility.
