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The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

This paper aims to document the impact of shadow banking on non-performing loans (NPLs) of publicly listed banks in an international setting.

This paper uses the data from 27 countries and various estimation strategies to test the arguments presented in this paper. The sample covers the period between 2002 and 2020.

The empirical results suggest that banks headquartered in countries with high shadow banking activity have fewer NPLs than otherwise similar banks headquartered in countries with low shadow banking activity. The findings remain qualitatively the same in different sub-samples and after replacing the main variables with their alternate proxies. The paper also shows that this relationship is sensitive to bank-specific characteristics. Moreover, the paper also indicates that the stringency of banking regulations weakens the relationship between shadow banking and NPLs.

The study’s data limitations prevent a detailed year-by-year analysis of NPLs and shadow banking, restricting insights into their evolving dynamics. In addition, the focus on country-level shadow banking data limits the exploration of how multinational banks’ activities in various jurisdictions impact individual banks’ NPLs.

The paper not only documents the effect of shadow banking on NPLs but also shows that the relationship between shadow banking and NPLs weakens as banking regulations become more stringent.