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

Darolles, Serge; Le Fol, Gaëlle; Lu, Yang; Sun, Ran (2019), Bivariate integer-autoregressive process with an application to mutual fund flows, Journal of Multivariate Analysis, 173, September 2019, p. 181-203. 10.1016/j.jmva.2019.02.015

Type
Article accepté pour publication ou publié
Date
2019
Journal name
Journal of Multivariate Analysis
Volume
173
Number
September 2019
Publisher
Elsevier
Pages
181-203
Publication identifier
10.1016/j.jmva.2019.02.015
Metadata
Show full item record
Author(s)
Darolles, Serge
Le Fol, Gaëlle
Lu, Yang
Sun, Ran
Abstract (EN)
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() 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.
Subjects / Keywords
Compound autoregressive process; Memory persistence; Mutual funds; Non-Linear forecasting
JEL
G23 - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
C53 - Forecasting and Prediction Methods; Simulation Methods
C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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