IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1402.2198.html
   My bibliography  Save this paper

Multi-scale Representation of High Frequency Market Liquidity

Author

Listed:
  • Anton Golub
  • Gregor Chliamovitch
  • Alexandre Dupuis
  • Bastien Chopard

Abstract

We introduce an event based framework of directional changes and overshoots to map continuous financial data into the so-called Intrinsic Network - a state based discretisation of intrinsically dissected time series. Defining a method for state contraction of Intrinsic Network, we show that it has a consistent hierarchical structure that allows for multi-scale analysis of financial data. We define an information theoretic measurement termed Liquidity that characterises the unlikeliness of price trajectories and argue that the new metric has the ability to detect and predict stress in financial markets. We show empirical examples within the Foreign Exchange market where the new measure not only quantifies liquidity but also acts as an early warning signal.

Suggested Citation

  • Anton Golub & Gregor Chliamovitch & Alexandre Dupuis & Bastien Chopard, 2014. "Multi-scale Representation of High Frequency Market Liquidity," Papers 1402.2198, arXiv.org.
    • Handle: RePEc:arx:papers:1402.2198
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1402.2198
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. B. Glattfelder & A. Dupuis & R. B. Olsen, 2010. "Patterns in high-frequency FX data: discovery of 12 empirical scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 599-614.
    2. Galluccio, S. & Caldarelli, G. & Marsili, M. & Zhang, Y.-C., 1997. "Scaling in currency exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 423-436.
    3. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    4. Muller, Ulrich A. & Dacorogna, Michel M. & Olsen, Richard B. & Pictet, Olivier V. & Schwarz, Matthias & Morgenegg, Claude, 1990. "Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis," Journal of Banking & Finance, Elsevier, vol. 14(6), pages 1189-1208, December.
    5. Alexandros Gabrielsen & Massimiliano Marzo & Paolo Zagaglia, 2011. "Measuring market liquidity: An introductory survey," Papers 1112.6169, arXiv.org.
    6. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vladimir Petrov & Anton Golub & Richard Olsen, 2019. "Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time," JRFM, MDPI, vol. 12(2), pages 1-31, April.
    2. Monira Essa Aloud, 2016. "Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 55-64.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. B. Glattfelder & A. Dupuis & R. B. Olsen, 2010. "Patterns in high-frequency FX data: discovery of 12 empirical scaling laws," Quantitative Finance, Taylor & Francis Journals, vol. 11(4), pages 599-614.
    2. Michele Caraglio & Fulvio Baldovin & Attilio L. Stella, 2021. "How Fast Does the Clock of Finance Run?—A Time-Definition Enforcing Stationarity and Quantifying Overnight Duration," JRFM, MDPI, vol. 14(8), pages 1-15, August.
    3. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2016. "Anomalous volatility scaling in high frequency financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 434-445.
    4. Seemann, Lars & McCauley, Joseph L. & Gunaratne, Gemunu H., 2011. "Intraday volatility and scaling in high frequency foreign exchange markets," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 121-126, June.
    5. Segnon, Mawuli & Lux, Thomas, 2013. "Multifractal models in finance: Their origin, properties, and applications," Kiel Working Papers 1860, Kiel Institute for the World Economy (IfW Kiel).
    6. Selçuk BAYRACI, 2017. "Long-memory, self-similarity and scaling of the long-term government bond yields: Evidence from Turkey and the USA," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(612), A), pages 71-82, Autumn.
    7. Noemi Nava & T. Di Matteo & Tomaso Aste, 2015. "Anomalous volatility scaling in high frequency financial data," Papers 1503.08465, arXiv.org, revised Dec 2015.
    8. Mercik, Szymon & Weron, Rafal, 2002. "Origins of scaling in FX markets," MPRA Paper 2294, University Library of Munich, Germany.
    9. Dai Pra, P. & Pigato, P., 2015. "Multi-scaling of moments in stochastic volatility models," Stochastic Processes and their Applications, Elsevier, vol. 125(10), pages 3725-3747.
    10. Seemann, Lars & Hua, Jia-Chen & McCauley, Joseph L. & Gunaratne, Gemunu H., 2012. "Ensemble vs. time averages in financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 6024-6032.
    11. Rama Cont & Jean-Philippe Bouchaud, 1997. "Herd behavior and aggregate fluctuations in financial markets," Science & Finance (CFM) working paper archive 500028, Science & Finance, Capital Fund Management.
    12. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    13. Broto, Carmen & Lamas, Matías, 2020. "Is market liquidity less resilient after the financial crisis? Evidence for US Treasuries," Economic Modelling, Elsevier, vol. 93(C), pages 217-229.
    14. Sensoy, Ahmet & Tabak, Benjamin M., 2016. "Dynamic efficiency of stock markets and exchange rates," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 353-371.
    15. Chang, Lo-Bin & Geman, Stuart, 2013. "Empirical scaling laws and the aggregation of non-stationary data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5046-5052.
    16. Hua, Jia-Chen & Chen, Lijian & Falcon, Liberty & McCauley, Joseph L. & Gunaratne, Gemunu H., 2015. "Variable diffusion in stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 221-233.
    17. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2020. "Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis," JRFM, MDPI, vol. 13(10), pages 1-18, October.
    18. Gu, Gao-Feng & Xiong, Xiong & Zhang, Yong-Jie & Chen, Wei & Zhang, Wei & Zhou, Wei-Xing, 2016. "Stylized facts of price gaps in limit order books," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 48-58.
    19. Los, Cornelis A., 1999. "Nonparametric testing of the high-frequency efficiency of the 1997 Asian foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 9(3-4), pages 265-289, November.
    20. Denzler, Stefan M. & Dacorogna, Michel M. & Muller, Ulrich A. & McNeil, Alexander J., 2006. "From default probabilities to credit spreads: Credit risk models do explain market prices," Finance Research Letters, Elsevier, vol. 3(2), pages 79-95, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1402.2198. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.