Finance-Insurance
Time: 10.00 am
Date:26th of June 2025
Room 3001

Jian CHEN (University of Sussex Business School) “Group Network Multivariate GARCH”

Abstract : Traditional multivariate generalised autoregressive conditional heteroskedasticity (GARCH) models (e.g., BEKK, DCC model) often suffer from the curse of dimensionality. A group network multivariate GARCH model is proposed in which the transitions of past variance and return shocks among assets are subject to an adjacency matrix and a latent group structure. This approach significantly reduces the number of parameters in high dimensions, thus facilitating estimation and forecasting. The theoretical properties of an estimator are developed that uses an optimisation algorithm estimating parameters and group memberships simultaneously. Simulation results confirm our theoretical findings. An empirical analysis is conducted on the S&P 100 constituents from 2015 to 2022 and shows that the model improves portfolio selection in out-of-sample forecasts compared to other models.

Joint work : Weidong Ma, University of Pennsylvania, Ganggang Xu, University of Miami