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2017 Inference for a mean-reverting stochastic process with multiple change points
Fuqi Chen, Rogemar Mamon, Matt Davison
Electron. J. Statist. 11(1): 2199-2257 (2017). DOI: 10.1214/17-EJS1282

Abstract

The use of an Ornstein-Uhlenbeck (OU) process is ubiquitous in business, economics and finance to capture various price processes and evolution of economic indicators exhibiting mean-reverting properties. The time at which structural transition representing drastic changes in the economic dynamics occur are of particular interest to policy makers, investors and financial product providers. This paper addresses the change-point problem under a generalised OU model and investigates the associated statistical inference. We propose two estimation methods to locate multiple change points and show the asymptotic properties of the estimators. An informational approach is employed in detecting the change points, and the consistency of our methods is also theoretically demonstrated. Estimation is considered under the setting where both the number and location of change points are unknown. Three computing algorithms are further developed for implementation. The practical applicability of our methods is illustrated using simulated and observed financial market data.

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Fuqi Chen. Rogemar Mamon. Matt Davison. "Inference for a mean-reverting stochastic process with multiple change points." Electron. J. Statist. 11 (1) 2199 - 2257, 2017. https://doi.org/10.1214/17-EJS1282

Information

Received: 1 April 2016; Published: 2017
First available in Project Euclid: 23 May 2017

zbMATH: 1364.60045
MathSciNet: MR3654824
Digital Object Identifier: 10.1214/17-EJS1282

Subjects:
Primary: 60G20 , 62P05
Secondary: 62M99

Keywords: consistent estimator , least sum of squared errors , maximum likelihood , Ornstein-Uhlenbeck process , PELT algorithm , segment neighbourhood search method , sequential analysis

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Vol.11 • No. 1 • 2017
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