SEMIPARAMETRIC VECTOR MEM
Article first published online: 18 SEP 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Journal of Applied Econometrics
Volume 28, Issue 7, pages 1067–1086, November/December 2013
How to Cite
Cipollini, F., Engle, R. F. and Gallo, G. M. (2013), SEMIPARAMETRIC VECTOR MEM. J. Appl. Econ., 28: 1067–1086. doi: 10.1002/jae.2292
- Issue published online: 25 NOV 2013
- Article first published online: 18 SEP 2012
- Manuscript Revised: 24 APR 2012
- Manuscript Received: 14 JAN 2009
Financial time series are often non-negative-valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element-by-element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process) is a suitable strategy. Its parameters can be estimated by generalized method of moments, bypassing the problem of specifying a multivariate distribution for the errors. Simulated results show the gains in efficiency relative to an equation-by-equation approach. A vMEM on several measures of volatility justifies a joint approach revealing full interdependence. Copyright © 2012 John Wiley & Sons, Ltd.