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.

Semiparametric Vector MEM / F.Cipollini; R.F.Engle; G.M.Gallo. - In: JOURNAL OF APPLIED ECONOMETRICS. - ISSN 0883-7252. - STAMPA. - 28:(2013), pp. 1067-1086. [10.1002/jae.2292]

Semiparametric Vector MEM

CIPOLLINI, FABRIZIO;GALLO, GIAMPIERO MARIA
2013

Abstract

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.
2013
28
1067
1086
F.Cipollini; R.F.Engle; G.M.Gallo
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/656522
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