The role of tail network topological characteristic in portfolio selection: A TNA-PMC model

Abstract

To improve the performance of a large portfolio selection, we consider the effect of tail network and propose a novel tail network-augmented parametric mean-conditional value-at-risk (CVaR) portfolio selection model labeled as TNA-PMC. First, we adopt the least absolute shrinkage and selection operator-quantile vector autoregression (LASSO-QVAR) approach to construct a tail network. Second, we parameterize the weights of the mean-CVaR model as a function of asset characteristics. Third, we incorporate the effect of the tail network topological characteristic, namely eigenvector centrality (EC), on the weights to construct the TNA-PMC model. After that, we apply the model to the empirical analysis on the Shanghai Stock Exchange 50 (SSE50) Index of China from January 2010 to September 2020. Our empirical results illustrate the effectiveness of the TNA-PMC model in two aspects. First, the TNA-PMC model clarifies the economic interpretation of the characteristics, such as the negative effective of EC on the portfolio weights. Second, the TNA-PMC model performs well in terms of achieving diversification and attractive risk-adjusted return.