IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v569y2021ics037843712100042x.html
   My bibliography  Save this article

Quantifying the randomness of the forex market

Author

Listed:
  • Delgado-Bonal, Alfonso
  • López, Álvaro García

Abstract

Currency markets are international networks of participants opened all day during weekdays without a supervisory entity. The precise value of an exchange pair is determined by the decisions of the central banks and the behavior of the speculators, whose actions can be determined on the spot or be related to previous decisions. All those decisions affect the complexity and predictability of the system, which are quantitatively analyzed in this paper. For this purpose, we compare the randomness of the most traded currencies in the forex market using the Pincus Index. We extend the development of this methodology to include multidimensionality in the embedding dimension, to capture the influence of the past in current decisions and to analyze different frequencies within the data with a multiscale approach. We show that, in general, the forex market is more predictable using one hour ticks than using daily data for the six major pairs, and present evidence suggesting that the variance is easier to predict for longer time frames.

Suggested Citation

  • Delgado-Bonal, Alfonso & López, Álvaro García, 2021. "Quantifying the randomness of the forex market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    • Handle: RePEc:eee:phsmap:v:569:y:2021:i:c:s037843712100042x
    DOI: 10.1016/j.physa.2021.125770
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712100042X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.125770?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Meng & Shang, Pengjian, 2018. "Analysis of financial time series using multiscale entropy based on skewness and kurtosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1543-1550.
    2. Bentes, Sónia R. & Menezes, Rui & Mendes, Diana A., 2008. "Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3826-3830.
    3. Liu, Yunxiao & Lin, Youfang & Wang, Jing & Shang, Pengjian, 2018. "Refined generalized multiscale entropy analysis for physiological signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 975-985.
    4. Jorion, Philippe, 1995. "Predicting Volatility in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 50(2), pages 507-528, June.
    5. Juan Bolea & Raquel Bailón & Esther Pueyo, 2018. "On the Standardization of Approximate Entropy: Multidimensional Approximate Entropy Index Evaluated on Short-Term HRV Time Series," Complexity, Hindawi, vol. 2018, pages 1-15, November.
    6. Restrepo, Juan F. & Schlotthauer, Gastón & Torres, María E., 2014. "Maximum approximate entropy and r threshold: A new approach for regularity changes detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 409(C), pages 97-109.
    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. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Arup Kumar Mohanty & Mazin Abed Mohammed & Alaa S. Al-Waisy & Seifedine Kadry & Jungeun Kim, 2022. "A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market," Mathematics, MDPI, vol. 10(19), pages 1-23, October.

    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. Woerner Jeannette H. C., 2003. "Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models," Statistics & Risk Modeling, De Gruyter, vol. 21(1), pages 47-68, January.
    2. René Garcia & Richard Luger & Eric Renault, 2000. "Asymmetric Smiles, Leverage Effects and Structural Parameters," Working Papers 2000-57, Center for Research in Economics and Statistics.
    3. Christoffersen, Peter & Heston, Steven & Jacobs, Kris, 2010. "Option Anomalies and the Pricing Kernel," Working Papers 11-17, University of Pennsylvania, Wharton School, Weiss Center.
    4. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.
    5. Hilscher, Jens, 2007. "Is the corporate bond market forward looking?," Working Paper Series 800, European Central Bank.
    6. Manabu Asai & Michael McAleer, 2017. "Forecasting the volatility of Nikkei 225 futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(11), pages 1141-1152, November.
    7. Ahoniemi, Katja & Lanne, Markku, 2009. "Joint modeling of call and put implied volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 239-258.
    8. Jose A. Lopez & Christian Walter, 1997. "Is implied correlation worth calculating? Evidence from foreign exchange options and historical data," Research Paper 9730, Federal Reserve Bank of New York.
    9. Szakmary, Andrew & Ors, Evren & Kyoung Kim, Jin & Davidson, Wallace III, 2003. "The predictive power of implied volatility: Evidence from 35 futures markets," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2151-2175, November.
    10. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    11. Steven Li & Qianqian Yang, 2009. "The relationship between implied and realized volatility: evidence from the Australian stock index option market," Review of Quantitative Finance and Accounting, Springer, vol. 32(4), pages 405-419, May.
    12. Chen, Ren-Raw & Hsieh, Pei-lin & Huang, Jeffrey, 2018. "Crash risk and risk neutral densities," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 162-189.
    13. David S. Bates, 2001. "The Market for Crash Risk," NBER Working Papers 8557, National Bureau of Economic Research, Inc.
    14. Cremers, Martijn & Fleckenstein, Matthias & Gandhi, Priyank, 2021. "Treasury yield implied volatility and real activity," Journal of Financial Economics, Elsevier, vol. 140(2), pages 412-435.
    15. Bent Jesper Christensen & Morten Ø. Nielsen, 2005. "The Implied-realized Volatility Relation With Jumps In Underlying Asset Prices," Working Paper 1186, Economics Department, Queen's University.
    16. Campa, Jose M. & Chang, P. H. Kevin & Reider, Robert L., 1998. "Implied exchange rate distributions: evidence from OTC option markets1," Journal of International Money and Finance, Elsevier, vol. 17(1), pages 117-160, February.
    17. Julián Andrada-Félix & Adrian Fernandez-Perez & Simón Sosvilla-Rivero, 2018. "Fear connectedness among asset classes," Applied Economics, Taylor & Francis Journals, vol. 50(39), pages 4234-4249, August.
    18. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    19. Tomas Björk & Bent Jesper Christensen, 1999. "Interest Rate Dynamics and Consistent Forward Rate Curves," Mathematical Finance, Wiley Blackwell, vol. 9(4), pages 323-348, October.
    20. William Pedersen, 1998. "Capturing all the information in foreign currency option prices: solving for one versus two implied variables," Applied Economics, Taylor & Francis Journals, vol. 30(12), pages 1679-1683.

    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:eee:phsmap:v:569:y:2021:i:c:s037843712100042x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.