A Linearization of the Portfolio Optimization Problem with General Risk Measures Under Multivariate Conditional Heteroskedastic Models

This study presents an example of the linearization of a complex mean-risk investment problem. The spectral risk measure is employed as a measure of risk and assets are assumed to have autocorrelation and conditionally heteroskedastic volatilities. Simulation results indicate that the proposed method saves a great deal of computational time. Empirical studies show that this strategy, implemented with certain trading frequency constraints, outperforms the equal-weighted portfolio, the classical mean-variance method, and the corresponding market index in Taiwan, the US, and Japan when considering transaction costs and different economic conditions.