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

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.