Semiparametric Vector MEM

F. Cipollini, R. Engle, G. Gallo
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引用次数: 63

Abstract

In financial time series analysis we encounter several instances of non–negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model). Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non–negative valued innovation process. A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks.
半参数向量MEM
在金融时间序列分析中,我们遇到了几个非负价值过程(交易量、交易、持续时间、实现波动率、日波动范围等)的实例,它们表现出聚类,可以建模为条件自回归尺度因子向量和多元创新过程(向量乘法误差模型)的乘积。与之前的建议相比,本文引入了两个新颖的点:一个更一般的规范,它将向量MEM与一个方程一个方程的规范区分开来;采用基于gmm的方法,绕过了指定一般多元非负价值创新过程的复杂问题。交易量、交易数量和已实现波动率的vMEM揭示了纽交所股票变量之间动态相互依赖关系的实证支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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