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Article: Optimal consumption and investment problems under GARCH with transaction costs
Title | Optimal consumption and investment problems under GARCH with transaction costs |
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Authors | |
Keywords | Consumption And Investment Problems Decomposition Stochastic Programming The Augmented Lagrangian The Garch Model |
Issue Date | 2005 |
Publisher | Physica-Verlag GmbH und Co. The Journal's web site is located at http://link.springer.de/link/service/journals/00186/ |
Citation | Mathematical Methods Of Operations Research, 2005, v. 61 n. 2, p. 219-237 How to Cite? |
Abstract | General multiperiod optimal consumption and investment problems with proportional transaction costs are investigated in this paper, a GARCH-type process is used to model the risky asset's return series so that its time-varying moments and conditional heteroskedasticity can be properly described. We model this kind of consumption and investment problems as dynamic stochastic optimization problems, which can easily cope with different utility functions and any number of time periods. The procedure to efficiently solve the resulting nonlinear stochastic optimization problem is discussed in detail and a parallelizable decomposition algorithm is devised. Numerical results show the suitability and promise of our methodology. © Springer-Verlag 2005. |
Persistent Identifier | http://hdl.handle.net/10722/172413 |
ISSN | 2023 Impact Factor: 0.9 2023 SCImago Journal Rankings: 0.535 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Chen, Z | en_US |
dc.contributor.author | Yuen, KC | en_US |
dc.date.accessioned | 2012-10-30T06:22:23Z | - |
dc.date.available | 2012-10-30T06:22:23Z | - |
dc.date.issued | 2005 | en_US |
dc.identifier.citation | Mathematical Methods Of Operations Research, 2005, v. 61 n. 2, p. 219-237 | en_US |
dc.identifier.issn | 1432-2994 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172413 | - |
dc.description.abstract | General multiperiod optimal consumption and investment problems with proportional transaction costs are investigated in this paper, a GARCH-type process is used to model the risky asset's return series so that its time-varying moments and conditional heteroskedasticity can be properly described. We model this kind of consumption and investment problems as dynamic stochastic optimization problems, which can easily cope with different utility functions and any number of time periods. The procedure to efficiently solve the resulting nonlinear stochastic optimization problem is discussed in detail and a parallelizable decomposition algorithm is devised. Numerical results show the suitability and promise of our methodology. © Springer-Verlag 2005. | en_US |
dc.language | eng | en_US |
dc.publisher | Physica-Verlag GmbH und Co. The Journal's web site is located at http://link.springer.de/link/service/journals/00186/ | en_US |
dc.relation.ispartof | Mathematical Methods of Operations Research | en_US |
dc.subject | Consumption And Investment Problems | en_US |
dc.subject | Decomposition | en_US |
dc.subject | Stochastic Programming | en_US |
dc.subject | The Augmented Lagrangian | en_US |
dc.subject | The Garch Model | en_US |
dc.title | Optimal consumption and investment problems under GARCH with transaction costs | en_US |
dc.type | Article | en_US |
dc.identifier.email | Yuen, KC: kcyuen@hku.hk | en_US |
dc.identifier.authority | Yuen, KC=rp00836 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1007/s001860400396 | en_US |
dc.identifier.scopus | eid_2-s2.0-18844401695 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-18844401695&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 61 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 219 | en_US |
dc.identifier.epage | 237 | en_US |
dc.identifier.isi | WOS:000229188800003 | - |
dc.publisher.place | Germany | en_US |
dc.identifier.scopusauthorid | Chen, Z=7409486774 | en_US |
dc.identifier.scopusauthorid | Yuen, KC=7202333703 | en_US |
dc.identifier.issnl | 1432-2994 | - |