Working Paper: NBER ID: w12690
Authors: Fabrizio Cipollini; Robert F. Engle; Giampiero M. Gallo
Abstract: The Multiplicative Error Model introduced by Engle (2002) for positive valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multi-variate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate positive valued random variables. We suggest the use of copulafunctions and of estimating equations to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. Empirical applications on volatility indicators are used to illustrate the gains over the equation by equation procedure.
Keywords: multiplicative error model; financial time series; copula functions; estimating equations; volatility indicators
JEL Codes: C01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| multiplicative error model (MEM) (C20) | dynamics of nonnegative valued processes (C69) |
| joint specification of the MEM (C30) | contemporaneous correlations among innovations (C10) |
| copula functions (C20) | dependence structure (D10) |
| copula functions (C20) | estimation accuracy (C13) |
| multivariate MEM framework (C39) | understanding of interactions between financial variables (E44) |
| multivariate MEM framework (C39) | better statistical properties for estimation (C51) |
| traditional equation-by-equation methods (C20) | different results (C29) |