Modelling multivariate volatilities via conditionally uncorrelated components

• 成果类型: Article
• 署名作者: Fan, Jianqing; Wang, Mingjin; Yao, Qiwei
• 署名单位: Princeton University; University of London; London School Economics & Political Science; Peking University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2008.00654.x
• 发表日期: 2008
• 页码: 679-702
• 关键词: garch models; ARCH; causality; rates

摘要

We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been established. A bootstrap method is proposed for testing the existence of CUCs. The methodology proposed is illustrated with both simulated and real data sets.