A0231
Title: Graphical copula GARCH modelling with dynamic conditional dependence
Authors: Shun Hin Chan - The Hong Kong University of Science and Technology (Hong Kong)
Amanda Chu - The Education University of Hong Kong (China)
Mike So - The Hong Kong University of Science and Technology (Hong Kong) [presenting]
Abstract: Traditional correlation models, such as the dynamic conditional correlation (DCC)-GARCH model, often omit the nonlinear dependencies in the tails. We aim to develop a framework to model the nonlinear dependencies dynamically among a large portfolio of stocks, namely the graphical copula GARCH (GC-GARCH) model. Motivated by the capital asset pricing model to allow high-dimensional modelling for large portfolios, the number of parameters can be greatly reduced by introducing conditional independence among stocks given the risk factors, such as the Hang Seng index in Hong Kong. The joint distribution of the risk factors is factorized using a directed acyclic graph (DAG) with pair-copula construction (PCC) to introduce flexibility. The DAG induces topological orders to the risk factors, which can be regarded as a list of directions of the flow of information. The conditional distributions among stock returns are also modeled using PCC. Dynamic conditional dependence structures are also incorporated to allow the parameters in the copulas to be time-varying. Three-stage estimation is used to estimate parameters in the marginal distributions, the risk factor copulas, and the stock copulas. In the investment experiments in the empirical study, we show that the GC-GARCH model produces more accurate conditional value-at-risk predictions and much higher cumulative portfolio values than the DCC-GARCH model.
