Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data

2015 ◽  
Vol 434 ◽  
pp. 84-98 ◽  
Author(s):  
Nikolas Gerlich ◽  
Stefan Rostek
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Serial Correlation ◽  
Financial Time Series ◽  
High Frequency Data ◽  
Frequency Data ◽  
Self Similarity ◽  
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

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

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

Measuring the self-similarity exponent in Lévy stable processes of financial time series

2013 ◽  
Vol 392 (21) ◽  
pp. 5330-5345 ◽  
Author(s):  
M. Fernández-Martínez ◽  
M.A. Sánchez-Granero ◽  
J.E. Trinidad Segovia
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
The Self ◽  
Stable Processes ◽  
Self Similarity ◽  
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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