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

2010 ◽  
Author(s):  
Guglielmo Maria Caporale ◽  
Luis A. Gil-Alana
Keyword(s):  
Time Series ◽  
Long Memory ◽  
High Frequency ◽  
Financial Time Series ◽  
Fractional Integration ◽  
Financial Time

High‐Frequency Trading and Financial Time‐Series Prediction with Spiking Neural Networks

Wilmott ◽  
2021 ◽  
Vol 2021 (113) ◽  
pp. 18-33
Author(s):  
Kang Gao ◽  
Wayne Luk ◽  
Stephen Weston
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Spiking Neural Networks ◽  
High Frequency Trading ◽  
Financial Time

Non-linear forecasting in high-frequency financial time series

2005 ◽  
Vol 353 ◽  
pp. 463-479 ◽  
Author(s):  
F. Strozzi ◽  
J.M. Zaldívar
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Financial Time ◽  
Non Linear

scholarly journals Searching for histogram patterns in USD/ZAR foreign exchange financial time series

2009 ◽  
Vol 6 (4) ◽  
pp. 575-584
Author(s):  
JH Van Rooyen
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
High Frequency ◽  
Financial Risk ◽  
Pattern Analysis ◽  
Financial Time Series ◽  
Rate Data ◽  
Data Set ◽  
Frequency Distributions ◽  
Financial Time

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

Jump detection with wavelets for high-frequency financial time series

2013 ◽  
Vol 14 (8) ◽  
pp. 1427-1444 ◽  
Author(s):  
Yi Xue ◽  
Ramazan Gençay ◽  
Stephen Fagan
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Jump Detection ◽  
Financial Time

Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting

2021 ◽  
Vol 62 ◽  
pp. 85-100
Author(s):  
Robert Garafutdinov ◽  
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Model Parameters ◽  
Memory Length ◽  
Financial Time ◽  
True Value ◽  
Arfima Model ◽  
Practical Recommendations

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.

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

2001 ◽  
Vol 5 (4) ◽  
pp. 281-302 ◽  
Author(s):  
Alan Kirman ◽  
Gilles Teyssière
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Financial Time Series ◽  
Financial Time ◽  
Microeconomic Models

Evolutionary-morphological learning machines for high-frequency financial time series prediction

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
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Learning Machines ◽  
Financial Time
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