Multiple Change-Points Estimation in GARCH Models

• Activity Number: 489
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Sichen Zhou* and Haipeng Xing
• Companies: SUNY Stony Brook and SUNY Stony Brook
• Keywords: change-points estimation ; GARCH model ; empirical Bayesian ; parameter change

Abstract

After a brief review of previous approaches to estimate multiple change-points in GARCH models, we describe an estimation procedure for multiple parameter changes in GARCH. This estimation procedure has attractive statistical and computational properties and yields explicit recursive formulas for piecewise constant parameters. Efficient estimators of the parameters of the model for the parameter jumps can be used in conjunction, yielding empirical Bayes estimates. The empirical Bayes approach is also applied to provide inference on the number and locations of change-points that partition the unknown parameter sequence into segments of equal values. Simulation studies of its performance and an illustrative application to the SP500 data are also given.