Changes of structure in financial time series and the GARCH model
AbstractIn this paper we propose a goodness of fit test that checks the resemblance of the spectral density of a GARCH process to that of the log-returns. The asymptotic behavior of the test statistics are given by a functional central limit theorem for the integrated periodogram of the data. A simulation study investigates the small sample behavior, the size and the power of our test. We apply our results to the S&P500 returns and detect changes in the structure of the data related to shifts of the unconditional variance. We show how a long range dependence type behavior in the sample ACF of absolute returns might be induced by these shifts.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0412003.
Length: 22 pages
Date of creation: 06 Dec 2004
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integrated periodogram; spectral distribution; functional central limit theorem; Kiefer--Muller process; Brownian bridge; sample autocorrelation; change point; GARCH process; long range dependence; IGARCH; non-stationarity;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-12-12 (All new papers)
- NEP-ECM-2004-12-12 (Econometrics)
- NEP-FIN-2004-12-12 (Finance)
- NEP-FIN-2004-12-15 (Finance)
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