Bandwidth selection by cross-validation for forecasting long memory financial time series

Richard T. Baillie, George Kapetanios, Fotis Papailias

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.
Original languageEnglish
Pages (from-to)129-143
Number of pages15
JournalJournal of Empirical Finance
Volume29
Early online date13 Apr 2014
DOIs
Publication statusPublished - Dec 2014

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Bandwidth
Long memory
Cross-validation
Financial time series
Long memory models
Semiparametric estimation
Simulation
Out-of-sample forecasting
Methodology
Model selection

Cite this

Baillie, Richard T. ; Kapetanios, George ; Papailias, Fotis. / Bandwidth selection by cross-validation for forecasting long memory financial time series. In: Journal of Empirical Finance. 2014 ; Vol. 29. pp. 129-143.
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Bandwidth selection by cross-validation for forecasting long memory financial time series. / Baillie, Richard T.; Kapetanios, George; Papailias, Fotis.

In: Journal of Empirical Finance, Vol. 29, 12.2014, p. 129-143.

Research output: Contribution to journalArticle

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