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Bivariate integer-autoregressive process with an application to mutual fund flows

Serge Darolles, Gaëlle Le Fol (), Yang Lu () and Ran Sun
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Gaëlle Le Fol: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique
Ran Sun: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: We propose a new family of bivariate nonnegative integer-autoregressive (BINAR) models for count process data. We first generalize the existing BINAR(1) model by allowing for dependent thinning operators and arbitrary innovation distribution. The extended family allows for intuitive interpretation, as well as tractable aggregation and stationarity properties. We then introduce higher order BINAR(p) and BINAR(∞) dynamics to accommodate more flexible serial dependence patterns. So far, the literature has regarded such models as computationally intractable. We show that the extended BINAR family allows for closed-form predictive distributions at any horizons and for any values of , which significantly facilitates non-linear forecasting and likelihood based estimation. Finally, a BINAR model with memory persistence is applied to open-ended mutual fund purchase and redemption order counts.

Keywords: Multivariate low event count process; memory persis- tence; Compound autoregressive process; Memory persistence; Mutual funds; Non-linear forecasting; liquidity risk; mutual funds MSC code: 62-15; JEL code: C32; C53 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-ets and nep-ore
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Published in Journal of Multivariate Analysis, Elsevier, 2019, 173, pp.181-203. ⟨10.1016/j.jmva.2019.02.015⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-02418967

DOI: 10.1016/j.jmva.2019.02.015

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