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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/27887

Title: Semiparametric vector MEM
Authors: Engle, Robert
Cipollini, Fabrizio
Gallo, Giampiero
Issue Date: 9-Feb-2009
Series/Report no.: FIN-08-041
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.
URI: http://hdl.handle.net/2451/27887
Appears in Collections:Finance Working Papers

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