IDEAS home Printed from https://ideas.repec.org/p/ime/imedps/26-e-03.html

Determinants of Liquidity in the Japanese Government Bond Market: An Interpretable Machine Learning Approach

Author

Listed:
  • Satoko Kojima

    (Director, Institute for Monetary and Economic Studies, Bank of Japan (Email: satoko.kojima@boj.or.jp))

  • Toshiyuki Sakiyama

    (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (Email: toshiyuki.sakiyama@boj.or.jp))

Abstract

Liquidity in government bond markets is critical for the functioning of financial markets. This paper studies the determinants of market liquidity, measured by price dispersion, by constructing various bond features using high-granularity data from the Bank of Japan Financial Network System and applying machine learning approaches. The main findings are threefold. First, the decomposition of the liquidity indicator into bond features reveals that the historical volatility of benchmark prices of Japanese government bonds has been the main driver of the liquidity indicator, while the contributions of the share of non- clearing participants' transactions and the share of the central bank's transactions and holdings have increased since around 2022. Second, some bond features affect the liquidity indicator non-linearly. For bond features such as the share of foreign financial institutions' transactions, the number of trading financial institutions, and the share of the central bank's holdings, the liquidity indicator improves as the values of these bond features increase, but deteriorates once they exceed certain thresholds. Third, bond features such as maturity, the historical volatility of benchmark prices, and the number of trading counterparties per institution affect the liquidity indicator by strongly interacting with other bond features.

Suggested Citation

  • Satoko Kojima & Toshiyuki Sakiyama, 2026. "Determinants of Liquidity in the Japanese Government Bond Market: An Interpretable Machine Learning Approach," IMES Discussion Paper Series 26-E-03, Institute for Monetary and Economic Studies, Bank of Japan.
  • Handle: RePEc:ime:imedps:26-e-03
    as

    Download full text from publisher

    File URL: https://www.imes.boj.or.jp/research/papers/english/26-E-03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kandrac, John & Schlusche, Bernd, 2013. "Flow effects of large-scale asset purchases," Economics Letters, Elsevier, vol. 121(2), pages 330-335.
    2. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    3. Gabor Pinter, 2023. "An anatomy of the 2022 gilt market crisis," Bank of England working papers 1019, Bank of England.
    4. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    5. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    6. Friewald, Nils & Jankowitsch, Rainer & Subrahmanyam, Marti G., 2012. "Illiquidity or credit deterioration: A study of liquidity in the US corporate bond market during financial crises," Journal of Financial Economics, Elsevier, vol. 105(1), pages 18-36.
    7. Toni Gravelle, 1999. "Liquidity of the Government of Canada Securities Market: Stylised Facts and Some Market Microstructure Comparisons to the United States Treasury Market," CGFS Papers chapters, in: Bank for International Settlements (ed.), Market Liquidity: Research Findings and Selected Policy Implications, volume 11, pages 1-37, Bank for International Settlements.
    8. Raphael Schestag & Philipp Schuster & Marliese Uhrig-Homburg, 2016. "Measuring Liquidity in Bond Markets," The Review of Financial Studies, Society for Financial Studies, vol. 29(5), pages 1170-1219.
    9. Jankowitsch, Rainer & Nashikkar, Amrut & Subrahmanyam, Marti G., 2011. "Price dispersion in OTC markets: A new measure of liquidity," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 343-357, February.
    10. Fleming, Michael & Nguyen, Giang & Rosenberg, Joshua, 2024. "How do Treasury dealers manage their positions?," Journal of Financial Economics, Elsevier, vol. 158(C).
    11. Richard Finlay & Dmitry Titkov & Michelle Xiang, 2023. "The Yield and Market Function Effects of the Reserve Bank of Australia's Bond Purchases," The Economic Record, The Economic Society of Australia, vol. 99(326), pages 359-384, September.
    12. 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.
    13. Mr. Fei Han & Dulani Seneviratne, 2018. "Scarcity Effects of Quantitative Easing on Market Liquidity: Evidence from the Japanese Government Bond Market," IMF Working Papers 2018/096, International Monetary Fund.
    14. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    15. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    16. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    17. Tetsuo Kurosaki & Yusuke Kumano & Kota Okabe & Teppei Nagano, 2015. "Liquidity in JGB Markets: An Evaluation from Transaction Data," Bank of Japan Working Paper Series 15-E-2, Bank of Japan.
    18. Vayanos, Dimitri & Wang, Tan, 2007. "Search and endogenous concentration of liquidity in asset markets," Journal of Economic Theory, Elsevier, vol. 136(1), pages 66-104, September.
    19. Schlepper, Kathi & Hofer, Heiko & Riordan, Ryan & Schrimpf, Andreas, 2020. "The Market Microstructure of Central Bank Bond Purchases," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(1), pages 193-221, February.
    20. Marcus Buckmann & Andreas Joseph, 2023. "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, vol. 19(4), pages 449-522, October.
    Full references (including those not matched with items on IDEAS)

    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. Goldstein, Michael A. & Namin, Elmira Shekari, 2023. "Corporate bond liquidity and yield spreads: A review," Research in International Business and Finance, Elsevier, vol. 65(C).
    2. Aris Kartsaklas, 2018. "Trader Type Effects On The Volatility‐Volume Relationship Evidence From The Kospi 200 Index Futures Market," Bulletin of Economic Research, Wiley Blackwell, vol. 70(3), pages 226-250, July.
    3. Shahzad, Hassan & Duong, Huu Nhan & Kalev, Petko S. & Singh, Harminder, 2014. "Trading volume, realized volatility and jumps in the Australian stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 414-430.
    4. Díaz, Antonio & Escribano, Ana, 2020. "Measuring the multi-faceted dimension of liquidity in financial markets: A literature review," Research in International Business and Finance, Elsevier, vol. 51(C).
    5. Iraklis Kollias & John Leventides & Vassilios G. Papavassiliou, 2024. "On the solution of games with arbitrary payoffs: An application to an over‐the‐counter financial market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1877-1895, April.
    6. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.
    7. repec:uts:finphd:39 is not listed on IDEAS
    8. Weigerding, Michael, 2023. "Long-term liquidity effects of large-scale asset purchase programs: Evidence from the euro covered bond market," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 244-264.
    9. Olga Cielinska & Andreas Joseph & Ujwal Shreyas & John Tanner & Michalis Vasios, 2017. "Gauging market dynamics using trade repository data: The case of the Swiss franc de-pegging," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    10. Slim, Skander & Dahmene, Meriam, 2016. "Asymmetric information, volatility components and the volume–volatility relationship for the CAC40 stocks," Global Finance Journal, Elsevier, vol. 29(C), pages 70-84.
    11. Díaz, Antonio & Escribano, Ana, 2022. "Liquidity dimensions in the U.S. corporate bond market," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 1163-1179.
    12. Cui, Xudong & Gong, Pu & Liu, Tong, 2025. "The disposition effect and market volatility prediction," International Review of Financial Analysis, Elsevier, vol. 108(PB).
    13. Vayanos, Dimitri & Wang, Jiang, 2013. "Market Liquidity—Theory and Empirical Evidence ," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1289-1361, Elsevier.
    14. Tribhuvan N. Puri & George C. Philippatos, 2008. "Asymmetric Volume‐Return Relation and Concentrated Trading in LIFFE Futures," European Financial Management, European Financial Management Association, vol. 14(3), pages 528-563, June.
    15. Ronald Mahieu & Rob Bauer, 1998. "A Bayesian analysis of stock return volatility and trading volume," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 671-687.
    16. Mougoué, Mbodja & Aggarwal, Raj, 2011. "Trading volume and exchange rate volatility: Evidence for the sequential arrival of information hypothesis," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2690-2703, October.
    17. Martin T. Bohl & Martin Stefan, 2020. "Return dynamics during periods of high speculation in a thinly traded commodity market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 145-159, January.
    18. Wilkoff, Sean & Yildiz, Serhat, 2023. "The behavior and determinants of illiquidity in the non-fungible tokens (NFTs) market," Global Finance Journal, Elsevier, vol. 55(C).
    19. Wang, Junbo & Wu, Chunchi, 2015. "Liquidity, credit quality, and the relation between volatility and trading activity: Evidence from the corporate bond market," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 183-203.
    20. Robert F. Engle & Jeffrey R. Russell, 1994. "Forecasting Transaction Rates: The Autoregressive Conditional Duration Model," NBER Working Papers 4966, National Bureau of Economic Research, Inc.
    21. Aldridge, Patrick & Cimon, David & Vala, Rishi, 2025. "Central Bank Crisis Interventions: A Review of the Recent Literature on Potential Costs," Journal of Financial Crises, Yale Program on Financial Stability (YPFS), vol. 7(4), pages 1-26, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:ime:imedps:26-e-03. 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: Kinken (email available below). General contact details of provider: https://edirc.repec.org/data/imegvjp.html .

    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.