Rethinking Long Memory and Structural Breaks in the Forward Premium

Xiao-Ming Li

Scottish Journal of Political Economy, 2014, vol. 61, issue 4, 455-485

Abstract: type="main" xml:id="sjpe12053-abs-0001">

This paper documents new results that the ability of structural breaks to explain away non-stationary long memory in the forward premium weakens considerably with higher-frequency data. For daily data, removing structural breaks does not make non-stationary long memory stationary, contrary to the evidence for monthly data reported in the recent literature. Simulating data on a daily basis, we show that using monthly data tends to overstate the importance of structural breaks, and obfuscate the true nature of persistence, in the forward premium. Our results thus corroborate earlier findings that long memory bears primary responsibility for the forward premium anomaly.

Date: 2014
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