Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ergebnis 3 von 538
Journal of forecasting, 2022-11, Vol.41 (7), p.1317-1337
2022
Volltextzugriff (PDF)

Details

Autor(en) / Beteiligte
Titel
Bayesian quantile forecasting via the realized hysteretic GARCH model
Ist Teil von
  • Journal of forecasting, 2022-11, Vol.41 (7), p.1317-1337
Ort / Verlag
Chichester: Wiley Periodicals Inc
Erscheinungsjahr
2022
Quelle
Wiley Blackwell Single Titles
Beschreibungen/Notizen
  • This research introduces a new model, a realized hysteretic GARCH, that is similar to a three‐regime nonlinear framework combined with daily returns and realized volatility. The setup allows the mean and volatility switching in a regime to be delayed when the hysteresis variable lies in a hysteresis zone. This nonlinear model presents explosive persistence and high volatility in Regime 1 in order to capture extreme cases. We employ the Bayesian Markov chain Monte Carlo (MCMC) procedure to estimate model parameters and to forecast volatility, value at risk (VaR), and expected shortfall (ES). A simulation study highlights the properties of the proposed MCMC methods, as well as their accuracy and satisfactory performance as quantile forecasting tools. We also consider two competing models, the realized GARCH and the realized threshold GARCH, for comparison and carry out Bayesian risk forecasting via predictive distributions on four stock markets. The out‐of‐sample period covers the recent 4 years by a rolling window approach and includes the COVID‐19 pandemic period. Among the realized models, the realized hysteretic GARCH model outperforms at the 1% level in terms of violation rates and backtests.
Sprache
Englisch
Identifikatoren
ISSN: 0277-6693
eISSN: 1099-131X
DOI: 10.1002/for.2876
Titel-ID: cdi_proquest_journals_2720221361

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX