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Bandwidth Selection by Cross-Validation for Forecasting Long Memory Financial Time Series

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Bandwidth Selection by Cross-Validation for Forecasting Long Memory Financial Time Series

Richard T. Baillie, George Kapetanios & Fotis Papailias

Journal of Empirical Finance
Available online 13 April 2014


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.

Keywords. Long memory, Fractional integration, Filtering, Cross-validation, Forecasting

DOI. 10.1016/j.jempfin.2014.04.002


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