High Frequency Financial Time Series Forecasting via Particle Filtering

2009 ◽  
Author(s):  
Zhang Gaoyu ◽  
Li Qiongfei ◽  
Luo Qing ◽  
Zhou Zhizhao
Keyword(s):  
Time Series ◽  
Particle Filtering ◽  
High Frequency ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time

scholarly journals Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data

2020 ◽  
Vol 136 ◽  
pp. 183-189 ◽  
Author(s):  
Nikolaos Passalis ◽  
Anastasios Tefas ◽  
Juho Kanniainen ◽  
Moncef Gabbouj ◽  
Alexandros Iosifidis
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Limit Order Book ◽  
Time Series Forecasting ◽  
Order Book ◽  
Limit Order ◽  
Bag Of Features ◽  
Financial Time

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

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