Independent Factor Autoregressive Conditional Density Model
提出独立因子自回归条件密度模型，利用独立因子结构生成时变高阶矩，在非椭圆多元分布中动态估计高阶联动并实现可行投资组合表示。基于14只MSCI股票指数iShares的1996至2010年数据，该模型在VaR预测和投资组合分配上优于CHICAGO和DCC模型。
In this article, we propose a novel Independent Factor Autoregressive Conditional Density (IFACD) model able to generate time-varying higher moments using an independent factor setup. Our proposed framework incorporates dynamic estimation of higher comovements and feasible portfolio representation within a non-elliptical multivariate distribution. We report an empirical application, using returns data from 14 MSCI equity index iShares for the period 1996 to 2010, and we show that the IFACD model provides superior VaR forecasts and portfolio allocations with respect to the Conditionally Heteroskedastic Independent Component Analysis of Generalized Orthogonal (CHICAGO) and DCC models.