Semiparametric Vector MEM
Universita di Firenze, DiSIA (Dipartimento di Statistica, Informatica, Applicazioni)
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)
Giampiero M. Gallo
Universita' di Firenze - Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti"
NYU Working Paper No. FIN-08-041
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: amore 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.
Number of Pages in PDF File: 21
Date posted: March 9, 2009