Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/31305 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
Working Paper No. 2006-13
Verlag: 
Rutgers University, Department of Economics, New Brunswick, NJ
Zusammenfassung: 
This chapter builds on previous work by Bhardwaj and Swanson (2004) who address the notion that many fractional I(d) processes may fall into the empty box” category, as discussed in Granger (1999). However, rather than focusing primarily on linear models, as do Bhardwaj and Swanson, we analyze the business cycle effects on the forecasting performance of these ARFIMA, AR, MA, ARMA, GARCH, and STAR models. This is done via examination of ex ante forecasting evidence based on an updated version of the absolute returns series examined by Ding, Granger and Engle (1993); and via the use of Diebold and Mariano (1995) and Clark and McCracken (2001) predictive accuracy tests. Results are presented for a variety of forecast horizons and for recursive and rolling estimation schemes. We find that the business cycle does not seem to have an effect on the relative forecasting performance of ARFIMA models.
Schlagwörter: 
Fractional integration
long horizon prediction
long memory
parameter estimation error
stock returns
JEL: 
C15
C22
C53
Dokumentart: 
Working Paper

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