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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.

The purpose of this study is to evaluate the effect of some internal features that influence the efficiency of non-bank financial institutions (NBFIs) in Bangladesh.

The study selected the top 15 Dhaka Stock Exchange (DSE)-listed NBFIs according to purposive sampling. The study period was from 2016 to 2020. Secondary data were collected from annual reports. The cost-to-income ratio was a dependent variable that was used as a proxy of operational efficiency. The ordinary least square regression technique was applied to measure the impact of firm-specific factors on efficiency.

Results showed that number of employees, branch number, firm size and deposit ratio have a significant effect on efficiency at 5% level. The number of branches and employees showed a negative impact, whereas firm size and deposit ratio showed a positive effect on the firms' efficiency. The deposit ratio is negatively related because deposit interest expenses were more than offset by interest income generation through the conversion of deposits into loans.

The study has practical and policy implications on NBFIs' managers, employees, shareholders, depositors, clients, regulatory authorities and government as efficiency enhancement would bring financial soundness.

This study shed light on some firm-specific factors that can be changed to increase operational efficiency or reduce the cost-to-income ratio. The novelty of the study is that it identified some significant associations between firm-specific factors and the operational efficiency of NBFIs.

Shadow banks or non-bank financial intermediaries (NBFIs) are facilitators of credit, especially in emerging market economies (EMEs). However, there are certain risks associated with them, such as their unchecked leverage and interconnectedness with the rest of the financial system. In light of this, the present study analyses the impact of the growth of shadow banks on the stability of the banking sector and the overall stability of the financial system. The authors further examine the effect of the growth of finance companies (a type of NBFIs) on financial stability.

The study employs data of 11 EMEs (monitored by the Financial Stability Board (FSB)) for the period 2002–2020 to examine the above relationships. Panel-corrected standard errors method and Driscoll–Kray standard error estimation are deployed to conduct the analysis.

The results signify that the growth of the shadow banking sector and the growth of lending to the shadow banking sector are negatively associated with the stability of the banking sector and increases the vulnerability of the financial system (overall instability). This implies that the higher the growth of the shadow banks, the higher the financial fragility. Finance companies are also found to negatively affect financial stability. These findings are validated by different estimation methods and point out the risks posed by the NBFI sector.

The extant study builds a composite index (Financial Vulnerability Index (FVI)) to measure financial stability; thus, the findings contribute to the evolving literature on shadow banks.

The purpose of this paper is to analyze the network path and internal mechanism of risks’ cross-contagion between shadow banks and design strategies for preventing risk infection between shadow banks.

Using the complex network theory, analyze the mechanism of risks’ cross-contagion between shadow banks from the credit network, business relationship network (BRN) and social network (SN); the cross-contagion mechanism using the structural equation model on the basis of China’s shadow banks is tested; based on the three risk infection paths, the prevention and control strategies for risk infection using the mathematical models of epidemic diseases are designed.

There are three network risk contagion paths between shadow banks. One, the credit network, risks are infected crossly mainly through debt and equity relationships; two, the BRN, risks are infected crossly mainly through business network and macro policy transmission; three, investor SN, risks are infected crossly mainly through individual SN and fractal relationships. The following three strategies for preventing risk’s cross-contagion between shadow banks: one, the in advance preventing strategy is more effective than the ex post control strategy; two, increasing the risk management coefficient; three, reducing the number of risk-infected submarkets.

The research of this study, especially the strategies for preventing the risks’ cross-contagion, could provide theoretical and practical guidance for regulatory authorities in formulating risk supervision measures.

The Financial Stability Board (FSB) determined that entities classified as shadow banking are of a credit nature because they are capable of affecting the financial system through the entry and exit of capital. This study aims at measuring the impact of shadow banking in the systemic risk in Chile. A sample of 91 institutions (Run) belonging to the mutual funds was used, with a series showing a continuous behaviour between 2004 and 2018.

The measurement is carried out using the conditional value at risk (CoVaR) methodology, which analyses the behaviour of an institution in a regular state against the same institution in a state of stress.

The results obtained reflect that liquidity mismatches do not have a relevant effect on the systemic risk, while the 2008 crisis does contribute to its decline.

There are less number of literature studies that apply statistical models regarding shadow banking, at least at a quantitative level, so this research is a beginning for other studies, supporting future authors in their new research as a basis.

El Consejo de Estabilidad Financiera determinó que las entidades clasificadas como Shadow Banking son de carácter crediticio debido a que son capaces de afectar al sistema financiero mediante la entrada y salida de capitales. Este estudio tiene como objetivo medir el impacto del Shadow Banking en el Riesgo Sistémico de Chile. Para esto se utilizó una muestra de 91 instituciones (Run) pertenecientes a los Fondos Mutuos, con series que muestran un comportamiento continuo entre 2004 y 2018.

La medición se lleva a cabo mediante la metodología CoVaR, la cual analiza la conducta de una institución en estado normal versus la misma institución en estado de estrés.

Los resultados obtenidos reflejan que los desajustes de liquidez no tienen un efecto relevante en el Riesgo Sistémico, mientras que la crisis del 2008 si contribuye a la disminución de este.

Existe muy poca literatura que aplica modelos estadísticos respecto al Shadow Banking, al menos a nivel cuantitativo, por lo que esta investigación es un inicio para otros estudios, apoyando como base a futuros autores en sus nuevas investigaciones.

Aims to examine the current financial scenario in Cyprus and to suggest medium‐term reforms to the institutional set‐up of Cypriot financial supervision.

Details the different sectors in Cyprus's financial set‐up and suggests measures necessary for remedial reform.

The suggested reforms should considerably enhance the consistency and extend the viability of the Cypriot financial supervision system without unduly upsetting its hitherto institutional balance or introducing unnecessary complications to its contemporary arrangements.

This represents a starting‐point for Cypriot financial reform, which has up to press been only marginally considered.

The paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.

The paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).

The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.

The study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.

Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.

The paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.

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.