Efficient Multipowers.
Multipower estimators, widespread for their robustness to the presence of jumps, are also useful for reducing the estimation error of integrated volatility powers even in the absence of jumps. Optimizing linear combinations of multipowers can indeed drastically reduce the variance with respect to tr...
| Publicado en: | Journal of Financial Econometrics Vol. 16; no. 4; pp. 629 - 660 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Fall2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=132427787&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 132427787 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: Fall2018 vid: 16 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 132427787 10.1093/jjfinec/nbx018 ppf: 629 ppct: 31 formats: tig: atl: Efficient Multipowers. aug: au: Kolokolov, Aleksey Renò, Roberto affil: Goethe University, SAFE Center Università di Verona su: United States Econometrics Market volatility Sampling errors Stock prices Mean square algorithms sug: subj: Econometrics United States Market volatility Sampling errors Stock prices Mean square algorithms keyword: efficiency jumps multipower quarticity threshold volatility efficiency jumps multipower quarticity threshold volatility ab: Multipower estimators, widespread for their robustness to the presence of jumps, are also useful for reducing the estimation error of integrated volatility powers even in the absence of jumps. Optimizing linear combinations of multipowers can indeed drastically reduce the variance with respect to traditional estimators. In the case of quarticity, we also prove that the optimal combination is a nearly efficient estimator, being arbitrarily close to the nonparametric efficiency bound as the number of consecutive returns employed diverges. We provide guidance on how to select the optimal number of consecutive returns to minimize mean square error. The implementation on U.S. stock prices corroborates our theoretical findings and further shows that our proposed quarticity estimator noticeably reduces the number of detected jumps, and improves the quality of volatility forecasts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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