High-dimensional penalized arch processes
提出一种通过惩罚方法（稀疏组Lasso）逐方程一致估计高维ARCH模型的通用方法，并研究了平稳性和正定性条件，通过模拟和金融组合管理评估了预测性能。
We introduce a general methodology to consistently estimate multidimensional ARCH models equation-by-equation, possibly with a very large number of parameters through penalization (Sparse Group Lasso). Some families of multidimensional ARCH models are proposed to tackle homogeneous or heterogeneous portfolios of assets. The corresponding conditions of stationarity and of positive definiteness are studied. We evaluate the relevance of such a strategy by simulation. The relative forecasting performances of our models are compared through the management of financial portfolios.