scholarly journals Long Memory and Fractional Integration in High Frequency Financial Time Series

Author(s):  
Guglielmo Maria Caporale ◽  
Luis A. Gil-Alana
2009 ◽  
Vol 6 (4) ◽  
pp. 575-584
Author(s):  
JH Van Rooyen

This study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from tick high frequency currency exchange rates over a period of one week. Shnoll et al. use the notion of macroscopic fluctuations (MF) to explain the behaviour of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study, which is a follow up of research by Van ZylBulitta, VH, Otte, R and Van Rooyen, JH, special emphasis is placed on the histogram pattern analysis of high frequency exchange rate data set. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings presented here widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics, more specifically the formation of discrete states and the repetition of histogram patterns


2013 ◽  
Vol 14 (8) ◽  
pp. 1427-1444 ◽  
Author(s):  
Yi Xue ◽  
Ramazan Gençay ◽  
Stephen Fagan

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.


2018 ◽  
Vol 42 ◽  
pp. 1-15 ◽  
Author(s):  
Ricardo de A. Araújo ◽  
Nadia Nedjah ◽  
José M. de Seixas ◽  
Adriano L.I. Oliveira ◽  
Silvio R. de L. Meira

Sign in / Sign up

Export Citation Format

Share Document