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dc.contributor.authorDettling, Marcel-
dc.contributor.authorBühlmann, Peter-
dc.date.accessioned2018-04-04T06:48:28Z-
dc.date.available2018-04-04T06:48:28Z-
dc.date.issued2004-
dc.identifier.issn0960-3107de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/4667-
dc.description.abstractAccurate volatility predictions are crucial for the successful implementation of risk management. The use of high frequency data approximately renders volatility from a latent to an observable quantity, and opens new directions to forecast future volatilities. The goals in this paper are: (i) to select an accurate forecasting procedure for predicting volatilities based on high frequency data from various standard models and modern prediction tools; (ii) to evaluate the predictive potential of those volatility forecasts for both the realized and the true latent volatility; and (iii) to quantify the differences using volatility forecasts based on high frequency data and using a GARCH model for low frequency (e.g. daily) data, and study its implication in risk management for two widely used risk measures. The pay-off using high frequency data for the true latent volatility is empirically found to be still present, but magnitudes smaller than suggested by simple analysis.de_CH
dc.language.isoende_CH
dc.publisherRoutledgede_CH
dc.relation.ispartofApplied Financial Economicsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectIDPde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleVolatility and risk estimation with linear and nonlinear methods based on high frequency datade_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeLondonde_CH
dc.identifier.doi10.1080/0960310042000243556de_CH
zhaw.funding.euNode_CH
zhaw.issue10de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end729de_CH
zhaw.pages.start717de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume14de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
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Dettling, M., & Bühlmann, P. (2004). Volatility and risk estimation with linear and nonlinear methods based on high frequency data. Applied Financial Economics, 14(10), 717–729. https://doi.org/10.1080/0960310042000243556
Dettling, M. and Bühlmann, P. (2004) ‘Volatility and risk estimation with linear and nonlinear methods based on high frequency data’, Applied Financial Economics, 14(10), pp. 717–729. Available at: https://doi.org/10.1080/0960310042000243556.
M. Dettling and P. Bühlmann, “Volatility and risk estimation with linear and nonlinear methods based on high frequency data,” Applied Financial Economics, vol. 14, no. 10, pp. 717–729, 2004, doi: 10.1080/0960310042000243556.
DETTLING, Marcel und Peter BÜHLMANN, 2004. Volatility and risk estimation with linear and nonlinear methods based on high frequency data. Applied Financial Economics. 2004. Bd. 14, Nr. 10, S. 717–729. DOI 10.1080/0960310042000243556
Dettling, Marcel, and Peter Bühlmann. 2004. “Volatility and Risk Estimation with Linear and Nonlinear Methods Based on High Frequency Data.” Applied Financial Economics 14 (10): 717–29. https://doi.org/10.1080/0960310042000243556.
Dettling, Marcel, and Peter Bühlmann. “Volatility and Risk Estimation with Linear and Nonlinear Methods Based on High Frequency Data.” Applied Financial Economics, vol. 14, no. 10, 2004, pp. 717–29, https://doi.org/10.1080/0960310042000243556.


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