Fabrizio Cipollini
Robert F. Engle
Giampiero M. Gallo

semiparametric vector mem (replication data)

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.

Data and Resources

Suggested Citation

Cipollini, Fabrizio; Engle, Robert F.; Gallo, Giampiero M. (2013): SEMIPARAMETRIC VECTOR MEM (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022321.0712116341