Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis

Martin Magris; Jiyeong Kim; Esa Räsänen; Juho Kanniainen

doi:10.1109/SSCI.2017.8280932

Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis

Martin Magris, Jiyeong Kim, Esa Räsänen, Juho Kanniainen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended fluctuation analysis (DFA). We address inter-event durations (order to order, trade to trade, cancel to cancel) as well as cross-event durations (time from order submission to its trade or cancel). We find strong evidence of long-range correlation, which is consistent across different stocks and variables. However, given the crossovers in the DFA fluctuation functions, our results indicate that the long-range correlation in inter-event durations becomes stronger over a longer time scale, i.e., when moving from a range of hours to days and further to months. We also observe interesting associations between the scaling exponent and a number of economic variables, in particular, in the inter-trade time series.

Original language	English
Title of host publication	2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Subtitle of host publication	November 27 - December 1 2017, Hawaii, USA.
Publisher	IEEE
Pages	485-491
Number of pages	7
ISBN (Electronic)	978-1-5386-2726-6
DOIs	https://doi.org/10.1109/SSCI.2017.8280932
Publication status	Published - 2017
Publication type	A4 Article in conference proceedings
Event	IEEE Symposium Series on Computational Intelligence (IEEE SSCI) - Honolulu Duration: 27 Nov 2017 → 1 Dec 2017

Conference

Conference	IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
City	Honolulu
Period	27/11/17 → 1/12/17

Keywords

DETRENDED FLUCTUATION ANALYSIS
HURST EXPONENT
PRICE

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Publication forum level 1

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10.1109/SSCI.2017.8280932

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@inproceedings{a9eee08bd62d48fdae539b4564b258ec,

title = "Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis",

abstract = "Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended fluctuation analysis (DFA). We address inter-event durations (order to order, trade to trade, cancel to cancel) as well as cross-event durations (time from order submission to its trade or cancel). We find strong evidence of long-range correlation, which is consistent across different stocks and variables. However, given the crossovers in the DFA fluctuation functions, our results indicate that the long-range correlation in inter-event durations becomes stronger over a longer time scale, i.e., when moving from a range of hours to days and further to months. We also observe interesting associations between the scaling exponent and a number of economic variables, in particular, in the inter-trade time series.",

keywords = "DETRENDED FLUCTUATION ANALYSIS, HURST EXPONENT, PRICE",

author = "Martin Magris and Jiyeong Kim and Esa R{\"a}s{\"a}nen and Juho Kanniainen",

year = "2017",

doi = "10.1109/SSCI.2017.8280932",

language = "English",

pages = "485--491",

booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)",

publisher = "IEEE",

address = "United States",

note = "IEEE Symposium Series on Computational Intelligence (IEEE SSCI) ; Conference date: 27-11-2017 Through 01-12-2017",

}

Magris, M, Kim, J, Räsänen, E & Kanniainen, J 2017, Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis. in 2017 IEEE Symposium Series on Computational Intelligence (SSCI): November 27 - December 1 2017, Hawaii, USA.. IEEE, pp. 485-491, IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Honolulu, 27/11/17. https://doi.org/10.1109/SSCI.2017.8280932

Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis. / Magris, Martin; Kim, Jiyeong; Räsänen, Esa et al.
2017 IEEE Symposium Series on Computational Intelligence (SSCI): November 27 - December 1 2017, Hawaii, USA.. IEEE, 2017. p. 485-491.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AU - Kim, Jiyeong

AU - Räsänen, Esa

AU - Kanniainen, Juho

PY - 2017

Y1 - 2017

N2 - Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended fluctuation analysis (DFA). We address inter-event durations (order to order, trade to trade, cancel to cancel) as well as cross-event durations (time from order submission to its trade or cancel). We find strong evidence of long-range correlation, which is consistent across different stocks and variables. However, given the crossovers in the DFA fluctuation functions, our results indicate that the long-range correlation in inter-event durations becomes stronger over a longer time scale, i.e., when moving from a range of hours to days and further to months. We also observe interesting associations between the scaling exponent and a number of economic variables, in particular, in the inter-trade time series.

AB - Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended fluctuation analysis (DFA). We address inter-event durations (order to order, trade to trade, cancel to cancel) as well as cross-event durations (time from order submission to its trade or cancel). We find strong evidence of long-range correlation, which is consistent across different stocks and variables. However, given the crossovers in the DFA fluctuation functions, our results indicate that the long-range correlation in inter-event durations becomes stronger over a longer time scale, i.e., when moving from a range of hours to days and further to months. We also observe interesting associations between the scaling exponent and a number of economic variables, in particular, in the inter-trade time series.

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KW - PRICE

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ER -

Long-range Auto-correlations in Limit Order Book Markets: Inter- and Cross-event Analysis

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