A0259
Title: The determinants of liquidity in the Japanese government bond markets: An interpretable machine learning approach
Authors: Toshiyuki Sakiyama - The Bank of Japan (Japan)
Satoko Kojima - The Bank of Japan (Japan) [presenting]
Abstract: Liquidity in government bond markets is critical for the functioning of financial markets. The aim is to study what and how bond features drive the liquidity measured by price dispersion by applying machine learning approaches to high-granularity data from the Bank of Japan financial network system. The main findings are threefold. First, the decomposition of the liquidity indicator into bond features reveals that the historical volatility of benchmark prices 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 2021. 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 it deteriorates once they exceed certain thresholds. Third, bond features such as maturity and the number of trading financial institutions interact strongly with other bond features in a way that changes how the interacted bond features affect the liquidity indicator.
