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DETECTING CHANGES IN GARCH(1,1) PROCESSES WITHOUT ASSUMING STATIONARITY
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DOI：10.1017/S026646662510011X.png)
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Abstract
En 中文
This article develops a new test to detect changes in generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) processes without imposing a stationary assumption. Specifically, the procedure tests the null hypothesis of a GARCH process with constant parameters, either in (strictly) stationary or explosive regimes, against the alternative hypothesis of parameter changes. We derive the limiting distribution of the test statistics and establish their asymptotic consistency. Monte Carlo simulations show that the proposed test has good size control and high power. We demonstrate a prototype application on a small group of stocks and report a further extensive application to more than ten thousand U.S. stocks.
Keywords:
MAXIMUM-LIKELIHOOD-ESTIMATION
AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY
CHANGE-POINT DETECTION
ASYMPTOTIC THEORY
INFERENCE
PARAMETER
VARIANCE
Journal
E
IF:
1
Papers:
29
Citations:
0