An evolutionary recursive algorithm in selecting statistical subset neural network/VDL filtering

We propose an evolutionary recursive algorithm, for the exact windowed case, to estimate subset vector discrete lag (SVDL) filters with a forgetting factor and an intercept variable. SVDL filtering is demonstrated as a basis for constructing a multi-layered polynomial neural network by Penm et al. (2000) The new proposed time update recursions allow users to update SVDL filters at consecutive time instants, and can show evolutionary changes detected in filter structures. With this new approach we are able to more effectively analyse complex relationships where the relevant financial time series have been generated from structures subject to evolutionary changes in their environment. An illustration of these procedures is presented to examine the integration between the Australian and the Japanese bond markets, and the USA and the UK bond markets, changed over the period. The proposed algorithms are also applicable to full-order vector discrete lag (VDL) filtering with a forgetting factor and an intercept.