Semiparametric Vector MEM
21 Pages Posted: 12 Oct 2008
Date Written: September 4, 2008
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.
Keywords: GARCH, GMM, MEM, NYSE, number of trades, realized volatility, volumes
JEL Classification: C32, C51
Suggested Citation: Suggested Citation