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An evolutionary recursive algorithm in selecting statistical subset neural network/VDL filtering. (English) Zbl 1105.62090

Summary: 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 J. H. W. Penm et al. [J. Time Ser. Anal. 21, No. 4, 389–412 (2000; Zbl 0974.62073)]. 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.

MSC:

62M20 Inference from stochastic processes and prediction
62M45 Neural nets and related approaches to inference from stochastic processes
62P05 Applications of statistics to actuarial sciences and financial mathematics

Citations:

Zbl 0974.62073
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References:

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