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