A0435
Title: Portfolio optimization based on dynamic networks and vine copulas
Authors: Shih-Feng Huang - National Central University (Taiwan) [presenting]
Abstract: The application of vine copulas combined with network methods is explored for portfolio optimization. It begins by eliminating inherent features such as autocorrelation, conditional heteroscedasticity, and volatility clustering in each financial time series using the de-GARCH technique. The similarity matrix of the multivariate de-GARCH series is then calculated to construct the global minimum spanning tree (MST), which helps identify suitable stocks for the portfolio. Subsequently, the local MST (LMST) is built for the selected stocks, and various vine copulas are employed based on the LMST to model the joint distribution of the selected stocks. This copula-network-based distribution is then used to set the weights of the selected stocks in the portfolio. The empirical investigation involves the component stocks of the S\&P 100 index from 2019 to 2023, using a rolling-window framework. The numerical results demonstrate that the proposed method yields satisfactory cumulative returns compared to competitors.
