Financial innovation and divisia monetary indices in Taiwan: a neural network approach

Jane M. Binner, Alicia M. Gazely, Shu Heng Chen

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    In this paper a weighted index measure of money using the ‘Divisia’ formulation is constructed for the Taiwan economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. This research extends an earlier study by Gazely and Binner by examining the theory that rapid financial innovation, particularly during the financial liberalization of the 1980s, has been responsible for the poor performance of conventional simple sum monetary aggregates. The Divisia index is adjusted in two ways to allow for the major financial innovations that Taiwan has experienced since the 1970s. The technique of neural networks is used to allow a completely flexible mapping of the variables and a greater variety of functional form than is currently achievable using conventional econometric techniques. Results suggest that superior tracking of inflation is possible for networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money appear to offer advantages over their simple sum counter parts as macroeconomic indicators.

    Original languageEnglish
    Pages (from-to)238-247
    JournalEuropean Journal of Finance
    Volume8
    Issue number2
    DOIs
    Publication statusPublished - Jun 2002

    Keywords

    • Divisia Money
    • Financial Innovation
    • Neural Networks

    ASJC Scopus subject areas

    • Economics, Econometrics and Finance (miscellaneous)

    Fingerprint

    Dive into the research topics of 'Financial innovation and divisia monetary indices in Taiwan: a neural network approach'. Together they form a unique fingerprint.

    Cite this