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On Comparing and Assessing Robustness of Some Popular Non-Stationary BINAR(1) Models

Author

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  • Yuvraj Sunecher

    (Department of Accounting and Finance, University of Technology Mauritius, Pointe-Aux-Sables 11110, Mauritius)

  • Naushad Mamode Khan

    (Department of Economics and Statistics, University of Mauritius, Reduit 80835, Mauritius)

Abstract

Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed under both time-variant and time-invariant conditions to capture features such as over-dispersion and non-stationarity in time series of counts. However, the quest for the most robust BINAR(1) models is still on. This paper considers specifically the family of BINAR(1)s with a non-diagonal cross-correlation structure and with unpaired innovation series. These assumptions relax the number of parameters to be estimated. Simulation experiments are performed to assess both the consistency of the estimators and the robust behavior of the BINAR(1)s under mis-specified innovation distribution specifications. The proposed BINAR(1)s are applied to analyze the intra-day transaction series of AstraZeneca and Ericsson. Diagnostic measures such as the root mean square errors (RMSEs) and Akaike information criteria (AICs) are also considered. The paper concludes that the BINAR(1)s with negative binomial and COM–Poisson innovations are among the most suitable models to analyze over-dispersed intra-day transaction series of stocks.

Suggested Citation

  • Yuvraj Sunecher & Naushad Mamode Khan, 2024. "On Comparing and Assessing Robustness of Some Popular Non-Stationary BINAR(1) Models," JRFM, MDPI, vol. 17(3), pages 1-13, February.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:3:p:100-:d:1348064
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    References listed on IDEAS

    as
    1. Vandna Jowaheer, 2002. "Analysing longitudinal count data with overdispersion," Biometrika, Biometrika Trust, vol. 89(2), pages 389-399, June.
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