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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
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    References listed on IDEAS

    as
    1. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Kandrac, John & Schlusche, Bernd, 2013. "Flow effects of large-scale asset purchases," Economics Letters, Elsevier, vol. 121(2), pages 330-335.
    13. 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.
    14. Gabor Pinter, 2023. "An anatomy of the 2022 gilt market crisis," Bank of England working papers 1019, Bank of England.
    15. 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.
    16. 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.
    17. Fleming, Michael & Nguyen, Giang & Rosenberg, Joshua, 2024. "How do Treasury dealers manage their positions?," Journal of Financial Economics, Elsevier, vol. 158(C).
    18. 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.
    19. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    20. 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.
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    JEL classification:

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

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