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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Time-varying mixture GARCH models and asymmetric volatility |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | North American Journal of Economics and Finance |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 1062-9408 |
| Volume | 26 |
| Page Range | 602 - 623 |
| Date | 2013 |
| Abstract Text | The class of mixed normal conditional heteroskedastic (MixN-GARCH) models, which couples a mixed normal distributional structure with GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as excellent out-of-sample forecasting performance, for financial asset returns. In this paper, we generalize the MixN-GARCH model by relaxing the assumption of constant mixing weights. Two different specifications with time--varying mixing weights are considered. In particular, by relating current weights to past returns and realized (component-wise) likelihood values, an empirically reasonable representation of Engle and Ng's (1993) news impact curve with an asymmetric impact of unexpected return shocks on future volatility is obtained. An empirical out-of-sample study confirms the usefulness of the new approach and gives evidence that the leverage effect in financial returns data is closely connected, in a non-linear fashion, to the time--varying interplay of mixture components representing, for example, various groups of market participants. |
| Digital Object Identifier | 10.1016/j.najef.2013.02.024 |
| Other Identification Number | merlin-id:8713 |
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