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

Serge Darolles (1) , Gaëlle Le Fol (1) , Yang Lu (2) , Ran Sun (1)

1 DRM - Dauphine Recherches en Management
2 CEPN - Centre d'Economie de l'Université Paris Nord

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.

Mots clés: 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

Domaines: Economies et finances

Serge Darolles, Gaëlle Le Fol, Yang Lu, Ran Sun. Bivariate integer-autoregressive process with an application to mutual fund flows. Journal of Multivariate Analysis, 2019, 173, pp.181-203. ⟨10.1016/j.jmva.2019.02.015⟩. ⟨halshs-02418967⟩