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Modelling with Dispersed Bivariate Moving Average Processes

Author

Listed:
  • Sunecher Yuvraj

    (Faculty of Business, Management and Finance, University of Technology Mauritius, Pointe-Aux-Sables, Mauritius)

  • Mamode Khan Naushad

    (Dept. of Economics and Statistics, University of Mauritius, Moka, Mauritius.)

  • Jowaheer Vandna

    (Faculty of Science, University of Mauritius, Reduit, Moka, Mauritius.)

Abstract

This paper proposes a non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) model where the respective innovations are marginal COM-Poisson and unrelated. As opposed to other such bivariate time series model, the dependence between the series in the above is constructed via the relation between the current series with survivor elements of the other series at the preceding time point. Under these assumptions, the BINMA(1) process is shown to accommodate different levels and combinations of over-, equi- and under-dispersion. Since under the non-stationary conditions, the joint likelihood function is hardly laborious to construct, a generalized quasi-likelihood (GQL) method of estimation is proposed to estimate the dynamic effects and dependence parameters. The asymptotic and consistency properties of the GQL estimators are also established. Monte-Carlo experiments and a real-life application to analyze intra-day stock transactions are presented to validate the proposed model and the estimation methodology.

Suggested Citation

  • Sunecher Yuvraj & Mamode Khan Naushad & Jowaheer Vandna, 2019. "Modelling with Dispersed Bivariate Moving Average Processes," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-19, January.
    • Handle: RePEc:bpj:jtsmet:v:11:y:2019:i:1:p:19:n:3
    DOI: 10.1515/jtse-2018-0009
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    References listed on IDEAS

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