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dc.contributor.authorPackham, Natalie-
dc.contributor.authorPapenbrock, Jochen-
dc.contributor.authorSchwendner, Peter-
dc.contributor.authorWoebbeking, Fabian-
dc.date.accessioned2018-08-29T14:44:08Z-
dc.date.available2018-08-29T14:44:08Z-
dc.date.issued2016-
dc.identifier.issn1469-7688de_CH
dc.identifier.issn1469-7696de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/9934-
dc.description.abstractStarting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.de_CH
dc.language.isoende_CH
dc.publisherRoutledgede_CH
dc.relation.ispartofQuantitative Financede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectExtreme eventde_CH
dc.subjectTail distributionde_CH
dc.subjectPortfolio protectionde_CH
dc.subjectTail-risk protectionde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleTail-risk protection trading strategiesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Wealth & Asset Management (IWA)de_CH
dc.identifier.doi10.1080/14697688.2016.1249512de_CH
zhaw.funding.euNode_CH
zhaw.issue5de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end744de_CH
zhaw.pages.start729de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume17de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Packham, N., Papenbrock, J., Schwendner, P., & Woebbeking, F. (2016). Tail-risk protection trading strategies. Quantitative Finance, 17(5), 729–744. https://doi.org/10.1080/14697688.2016.1249512
Packham, N. et al. (2016) ‘Tail-risk protection trading strategies’, Quantitative Finance, 17(5), pp. 729–744. Available at: https://doi.org/10.1080/14697688.2016.1249512.
N. Packham, J. Papenbrock, P. Schwendner, and F. Woebbeking, “Tail-risk protection trading strategies,” Quantitative Finance, vol. 17, no. 5, pp. 729–744, 2016, doi: 10.1080/14697688.2016.1249512.
PACKHAM, Natalie, Jochen PAPENBROCK, Peter SCHWENDNER und Fabian WOEBBEKING, 2016. Tail-risk protection trading strategies. Quantitative Finance. 2016. Bd. 17, Nr. 5, S. 729–744. DOI 10.1080/14697688.2016.1249512
Packham, Natalie, Jochen Papenbrock, Peter Schwendner, and Fabian Woebbeking. 2016. “Tail-Risk Protection Trading Strategies.” Quantitative Finance 17 (5): 729–44. https://doi.org/10.1080/14697688.2016.1249512.
Packham, Natalie, et al. “Tail-Risk Protection Trading Strategies.” Quantitative Finance, vol. 17, no. 5, 2016, pp. 729–44, https://doi.org/10.1080/14697688.2016.1249512.


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