A Predictive Comparison of Some Simple Long Memory and Short Memory Models of Daily U.S. Stock Returns, With Emphasis on Business Cycle Effects

Nonlinear Time Series Analysis of Business Cycles, Forthcoming

29 Pages Posted: 28 Feb 2007 Last revised: 11 Sep 2008

See all articles by Geetesh Bhardwaj

Geetesh Bhardwaj

SummerHaven Investment Management

Norman R. Swanson

Rutgers University - Department of Economics; Rutgers, The State University of New Jersey - Department of Economics

Abstract

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.

Keywords: fractional integration, long memory, parameter estimation error, stock returns, long

JEL Classification: C15, C22, C53

Suggested Citation

Bhardwaj, Geetesh and Swanson, Norman Rasmus and Swanson, Norman Rasmus, A Predictive Comparison of Some Simple Long Memory and Short Memory Models of Daily U.S. Stock Returns, With Emphasis on Business Cycle Effects. Nonlinear Time Series Analysis of Business Cycles, Forthcoming, Available at SSRN: https://ssrn.com/abstract=965961

Geetesh Bhardwaj

SummerHaven Investment Management ( email )

Soundview Plaza,
1266 East Main Street
Stamford, CT 06902
United States

Norman Rasmus Swanson (Contact Author)

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States
848-932-7432 (Phone)

HOME PAGE: http://econweb.rutgers.edu/nswanson/

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