Proximity-Structured Multivariate Volatility Models
针对多元波动率模型中参数随维度快速增长导致的维数灾难问题，提出基于经济邻近性权重矩阵的结构化参数化方法，可缓解或解决该问题，并给出识别与估计条件。
In many multivariate volatility models, the number of parameters increases faster than the cross-section dimension, hence creating a curse of dimensionality problem. This paper discusses specification and identification of structured parameterizations based on weight matrices induced by economic proximity. It is shown that structured specifications can mitigate or even solve the curse of dimensionality problem. Identification and estimation of structured specifications are analyzed, rank and order conditions for identification are given and the specification of weight matrices is discussed. Several structured specifications compare well with alternatives in modelling conditional covariances of six returns from the New York Stock Exchange.