Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/216961
Authors: 
Becker, Janis
Hollstein, Fabian
Prokopczuk, Marcel
Sibbertsen, Philipp
Year of Publication: 
2019
Series/Report no.: 
Hannover Economic Papers (HEP) No. 661
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.
Subjects: 
Long memory
beta
persistence
forecasting
predictability
JEL: 
C58
G15
G12
G11
Document Type: 
Working Paper

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