Microeconomic Models for Long Memory in the Volatility of Financial Time Series

Author(s):  
Alan Kirman ◽  
Gilles Teyssière



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.



2014 ◽  
Vol 29 ◽  
pp. 129-143 ◽  
Author(s):  
Richard T. Baillie ◽  
George Kapetanios ◽  
Fotis Papailias




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






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