Forecasting risk via realized GARCH, incorporating the realized range
Field | Value | Language |
dc.contributor.author | Richard, Gerlach | |
dc.contributor.author | Chao, Wang | |
dc.date.accessioned | 2014-11-07 | |
dc.date.available | 2014-11-07 | |
dc.date.issued | 2014-11-07 | |
dc.identifier.uri | http://hdl.handle.net/2123/12235 | |
dc.description.abstract | The realized GARCH framework is extended to incorporate the realized range, and the intra-day range, as potentially more efficient series of information than re- alized variance or daily returns, for the purpose of volatility and tail risk forecasting in a financial time series. A Bayesian adaptive Markov chain Monte Carlo method is employed for estimation and forecasting. Compared to a range of well known parametric GARCH models, predictive log-likelihood results across six market in- dex return series favor the realized GARCH models incorporating the realized range. Further, these same models also compare favourably for tail risk forecasting, both during and after the global financial crisis. | en_AU |
dc.language.iso | en_US | en_AU |
dc.publisher | Business Analytics. | |
dc.relation.ispartofseries | BAWP-2014-06 | en_AU |
dc.subject | Realized Range | en_AU |
dc.subject | Intra-day Range | en_AU |
dc.subject | Realized Variance | en_AU |
dc.subject | Realized GARCH | en_AU |
dc.subject | Predictive Likelihood | en_AU |
dc.subject | Tail Risk Forecasting | en_AU |
dc.title | Forecasting risk via realized GARCH, incorporating the realized range | en_AU |
dc.type | Working Paper | en_AU |
dc.contributor.department | Discipline of Business Analytics | en_AU |
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