Abstract
AbstractTo solve a large portfolio selection, we propose a novel norm constrained time‐varying minimum variance model with DCC‐MIDAS, labelled as NC‐MVP‐DCC‐MIDAS. It applies the DCC‐MIDAS model to improve the estimation of dynamic correlations among financial assets by exploiting rich information contained in mixed frequency data. Additionally, it imposes norm constraints on the minimum variance portfolio with the elastic‐net penalty to pick a reasonable number of assets and prevent extreme positions in the resulting portfolio. Its superiority is illustrated via empirical studies on the construction of optimal sparse and stable portfolios with the constituent stocks in the Shanghai Stock Exchange (SSE) 50 Index of China.
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