scholarly journals Permutation approach, high frequency trading and variety of micro patterns in financial time series

2014 ◽  
Vol 413 ◽  
pp. 25-30 ◽  
Author(s):  
Cina Aghamohammadi ◽  
Mehran Ebrahimian ◽  
Hamed Tahmooresi
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
High Frequency Trading ◽  
Financial Time ◽  
Micro Patterns

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

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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