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

2014 ◽  
Vol 29 ◽  
pp. 129-143 ◽  
Author(s):  
Richard T. Baillie ◽  
George Kapetanios ◽  
Fotis Papailias
2021 ◽  
Vol 62 ◽  
pp. 85-100
Author(s):  
Robert Garafutdinov ◽  

The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.


2010 ◽  
Vol 20 (6) ◽  
pp. 487-500 ◽  
Author(s):  
Luiz Renato Lima ◽  
Zhijie Xiao

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