Blue Chip Italian Bank Stocks: Chain Graph models for VAR And MARCH parameters shrinking

Andrea Pierini

No 205, Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre

Abstract: Abstract In this paper Chain Graph models are applied to a multivariate time series of blue chip Italian bank stock returns, in order to construct graphs with minimum BIC among a particular class of graphs called decomposable, which have the desirable property of a closed form estimation . Firstly a chain graph is built for present and past values of the time series in order to reduce the parameters of a VAR(1) model estimation, setting to zero the parameters related to non-edges in the graphs. Then another chain graph is built for present and past values of the squared residuals of the previous estimated model. An MARCH(1) model is so constructed by restricting to zero the parameters which are not indicated by these graphs. In this way a great reduction of parameters is put in place, using the opportune multidimensional modelling only if it is necessary. The parameter shrinking doesn’t affect the return and standard deviation forecasts while improving the efficiency of the estimations. This approach seems powerful in that it can use the methodology of chain graph to identify the needed past to present relationships for multivariate time series

Keywords: Chain Graph Model; VAR; MARCH; Blue Chip Bank returns. (search for similar items in EconPapers)
Pages: 21
Date: 2016-02
New Economics Papers: this item is included in nep-ecm
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