A1376
Title: Portfolio optimization via dynamic networks and vine copulas
Authors: Tzu-Hsin Chien - National Central University (Taiwan) [presenting]
Meihui Guo - National Sun Yat-sen University (Taiwan)
Shih-Feng Huang - National Central University (Taiwan)
Abstract: The application of vine copulas combined with network-based methods for portfolio optimization is explored. A DeGARCH technique is employed to preprocess each series to address inherent characteristics such as autocorrelation, conditional heteroscedasticity, and volatility clustering in financial time series. A similarity matrix is then computed from the multivariate DeGARCH data and used to construct a global minimum spanning tree (MST), which facilitates the identification of suitable stocks for portfolio construction. Subsequently, a local MST (LMST) is built from the selected stocks, and a vine copula is applied based on the LMST structure to model the joint distribution. This copula-network based distribution is then used to determine portfolio weights. An empirical analysis conducted on component stocks of the S\&P 100 index over the 2019-2023 period using a rolling-window framework shows that the proposed method achieves competitive cumulative returns compared to benchmark approaches.
