dc.identifier.uri |
http://dx.doi.org/10.15488/9785 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/9842 |
|
dc.contributor.author |
Becker, Janis
|
|
dc.contributor.author |
Hollstein, Fabian
|
|
dc.contributor.author |
Prokopczuk, Marcel
|
|
dc.contributor.author |
Sibbertsen, Philipp
|
|
dc.date.accessioned |
2020-04-17T14:12:06Z |
|
dc.date.available |
2020-04-17T14:12:06Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Becker, Janis; Hollstein, Fabian; Prokopczuk, Marcel; Sibbertsen, Philipp: The Memory of Beta Factors. Hannover : Institutionelles Repositorium der Leibniz Universität Hannover, 2020. DOI: https://doi.org/10.15488/9785 |
|
dc.description.abstract |
Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long- memory model reliably provides superior beta forecasts compared to all alternatives. Finally, we document the relation of firm characteristics with the forecast error differentials that result from inadequately imposing short-memory or random walk instead of long-memory processes. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Hannover : Institutionelles Repositorium der Leibniz Universität Hannover |
|
dc.rights |
CC BY 3.0 DE |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/3.0/de/ |
|
dc.subject |
Long memory |
eng |
dc.subject |
beta |
eng |
dc.subject |
persistence |
eng |
dc.subject |
forecasting |
eng |
dc.subject |
predictability |
eng |
dc.subject.ddc |
330 | Wirtschaft
|
|
dc.title |
The Memory of Beta Factors |
eng |
dc.type |
Report |
|
dc.type |
Text |
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dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|