Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/3031 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorLux, Thomasen
dc.date.accessioned2009-01-28T14:22:40Z-
dc.date.available2009-01-28T14:22:40Z-
dc.date.issued2003-
dc.identifier.piurn:nbn:de:101:1-200911022352en
dc.identifier.urihttp://hdl.handle.net/10419/3031-
dc.description.abstractMulti-fractal processes have been proposed as a new formalism for modeling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found to characterize virtually all financial prices. Furthermore, elementary variants of multi-fractal models are very parsimonious formalizations as they are essentially one-parameter families of stochastic processes. The aim of this paper is to provide the characteristics of a causal multi-fractal model (replacing the earlier combinatorial approaches discussed in the literature), to estimate the parameters of this model and to use these estimates in forecasting financial volatility. We use the auto-covariances of log increments of the multi-fractal process in order to estimate its parameters consistently via GMM (Generalized Method of Moment). Simulations show that this approach leads to essentially unbiased estimates, which also have much smaller root mean squared errors than those obtained from the traditional ?scaling? approach. Our empirical estimates are used in out-of-sample forecasting of volatility for a number of important financial assets. Comparing the multi-fractal forecasts with those derived from GARCH and FIGARCH models yields results in favor of the new model: multi-fractal forecasts dominate all other forecasts in one out of four cases considered, while in the remaining cases they are head to head with one or more of their competitors.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2003-13en
dc.subject.jelC20en
dc.subject.jelG12en
dc.subject.ddc330en
dc.subject.keywordmulti-fractalityen
dc.subject.keywordfinancial volatilityen
dc.subject.keywordforecastingen
dc.subject.stwKapitalertragen
dc.subject.stwBörsenkursen
dc.subject.stwVolatilitäten
dc.subject.stwPrognoseverfahrenen
dc.subject.stwPhysiken
dc.subject.stwStochastischer Prozessen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwTheorieen
dc.titleThe multi-fractal model of asset returns: Its estimation via GMM and its use for volatility forecasting-
dc.typeWorking Paperen
dc.identifier.ppn368180670en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:1123en

Datei(en):
Datei
Größe
778.79 kB





Publikationen in EconStor sind urheberrechtlich geschützt.