Arbeitspapier
Real time detection of structural breaks in GARCH models
A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. The method conveniently deals with the path dependence problem that arises in these type of models. The performance of the method is shown to work well using simulated data. Applied to daily NASDAQ returns, the evidence favors a partial structural break specification in which only the intercept of the conditional variance equation has breaks compared to the full structural break specification in which all parameters are subject to change. The empirical application underscores the importance of model assumptions when investigating breaks. A model with normal return innovations results in strong evidence of breaks; while more flexible return distributions such as t-innovations or a GARCH-jump mixture model still favors breaks but indicates much more uncertainty regarding the time and impact of them.
- Sprache
-
Englisch
- Erschienen in
-
Series: Bank of Canada Working Paper ; No. 2009-31
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Forecasting Models; Simulation Methods
- Thema
-
Econometric and statistical methods
Financial markets
Finanzmarkt
Prognose
Strukturbruch
ARCH-Modell
Statistische Methode
- Ereignis
-
Geistige Schöpfung
- (wer)
-
He, Zhongfang
Maheu, John M.
- Ereignis
-
Veröffentlichung
- (wer)
-
Bank of Canada
- (wo)
-
Ottawa
- (wann)
-
2009
- DOI
-
doi:10.34989/swp-2009-31
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- He, Zhongfang
- Maheu, John M.
- Bank of Canada
Entstanden
- 2009
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