Implementation of Markowitz Mean-Variance Model Based on Matrix-Valued Factor Algorithm

Accurate modeling and prediction of the financial asset covariance matrix plays an important role in building an effective portfolio. In this paper, a predictable matrix value factor model based on Cholesky decomposition and vector autoregressive method is adopted. This method significantly reduces the number of parameters to be estimated, effectively avoiding the accumulation of estimation errors and the dimensionality of low-dimensional matrices. Empirical analysis indicates that the dynamic investment portfolio can be obtained by constructing the investment strategy with minimum variance. The model can follow the market dynamics accurately and shows satisfactory forecasting ability, and the numerical experiments in the high-performance computing environment of supercomputer show that this model has good validity and scalability.