A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points
Journal Title: Review of Economics & Finance - Year 2017, Vol 8, Issue 2
Abstract
This paper discusses GARCH models with multiple change-points and proposes a mixture localized likelihood method to estimate the piecewise constant GARCH parameters. The proposed method is statistically and computationally attractive as it synthesizes two degenerated and basic inference procedures. A bounded complexity mixture approximation, whose computational complexity is linear only, is also proposed for the estimates of time-varying GARCH parameters. These procedures are further applied to solve challenging problems such as inference on the number and locations of change-points that partition the unknown parameter sequence into segments of constant values. An illustrative analysis of the S&P500 index is provided.
Authors and Affiliations
Haipeng Xing, Hongsong Yuan, Sichen Zhou
Keywords
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