LPM is a downside risk measure, and can characterize different downside preferences, so it is a "respectable" risk measure in the portfolio optimization problem. Harlow analyses a mean-LPM portfolio selection model using realized data. In this paper, we analyze the problem using predicted data. To account for the time variation, leverage effect and fat-tails in the financial time series, we introduce the AR(1)-GJR(1,1)-EVT model to model the marginal distribution of individual asset. The t Copula is used to model the nonlinear dependence structure between assets. The empirical study based on China stock markets shows that the mean-lower partial moment model yields a better performance if the predicted data instead of realized data are used.
Lower partial momentPortfolio selectionTime series modelCopula function
国家科技期刊平台
NSTL
万方数据
维普
