Monetary policy, state-dependent bank capital requirements and the role of non-bank financial intermediaries – Bank Underground

The global financial landscape has undergone significant transformation in recent years with NBFIs becoming increasingly prominent in credit provision. Their expanded activities have contributed to a more intricate system, presenting new challenges for macroprudential policy and supervision (Buchak et al (2018)).

While some studies highlight the risks posed by NBFIs, particularly due to their limited regulatory oversight and potential to amplify systemic vulnerabilities (Plantin (2014), Gennaioli et al (2013)), others point to their role in improving market efficiency and diversifying funding sources (Ordoñez (2018)). The ultimate impact of NBFIs on financial stability and welfare remains an open question, especially during periods of economic stress. Our paper contributes to this debate by examining how NBFIs influence the economy’s vulnerability to severe downturns − those rare but impactful episodes that can pose outsized risks to financial stability − and the transmission of monetary policy.

To explore these dynamics, we develop a structural model that reflects the interactions between traditional banks, NBFIs, and monetary policy. This framework allows us to assess how financial structures affect the economy’s response to shocks. By focusing on episodes of heightened financial fragility, our aim is to provide insights that can help policymakers balance the goals of stability and efficiency in an evolving financial landscape.

Methodology

We develop a microfounded DSGE model that places state-dependent capital requirements for commercial banks at the heart of the financial system. Unlike traditional models that assume banks face symmetric capital adjustment costs (Gerali et al (2010)), our framework introduces a crucial non-linearity: capital adjustment costs only activate when a bank’s capital ratio dips below a regulatory threshold, otherwise remaining inactive. This asymmetry means that when banks are well-capitalised, they face no penalty, but as soon as their capital falls short, loan-deposit spreads rise, reflecting heightened funding costs.

While our methodology enables the analysis of state-dependent dynamics, it still retains some of the simplifying features of Gerali et al (2010): it does not account for risk and uncertainty, and the way banks are required to hold extra capital relies on simplified assumptions.

The financial sector in our model explicitly distinguishes between two types of lenders: regulated commercial banks, subject to capital requirements and protected by resolution regimes and deposits insurance; and NBFIs, which are not directly regulated. NBFIs depend on market discipline to maintain investor trust, operating under an incentive compatibility constraint that ensures their actions remain credible in the eyes of savers and investors. Within this competitive landscape, commercial banks possess some market power when setting interest rates, whereas NBFIs operate in perfectly competitive markets (Gebauer and Mazelis (2023)). In our framework, banks and NBFIs compete but do not interact directly; we therefore abstract from potential interlinkages such as banks’ exposure to NBFIs through activities like prime brokerage.

We utilise this model to examine how the economy reacts to monetary policy shocks in both the short and long term. Specifically, we distinguish the effects of asymmetric capital requirements, as opposed to symmetric ones, on the consequences of a rise in the policy rate, and we assess the specific role that NBFIs play in the transmission mechanism compared to a scenario in which only banks act as financial intermediaries.

Beyond average outcomes, we focus on how these factors shape the economy during severe downturns − what economists call the ‘left tail’ of the GDP distribution, meaning situations where GDP falls to very low levels. To capture these rare but costly events, we simulate the model under a wide range of economic conditions and focus on extreme scenarios such as deep recessions or financial stress. This approach allows us to evaluate how the inclusion of NBFIs affects the likelihood and severity of rare but costly events.

We also account for the zero lower bound on interest rates, given its relevance in recent stress episodes. Finally, we complement our analysis with a welfare analysis, where welfare is defined as the weighted sum of the individual welfare of savers in the economy and the entrepreneur. This approach enables us to compare long-term outcomes with and without NBFIs, thereby assessing their broader contribution to financial stability and economic efficiency.

Findings

Chart 1 illustrates impulse response functions following a 1% monetary policy shock, contrasting two versions of the model: one featuring only banks (red lines) and the other incorporating NBFIs (blue lines). Each subplot reports percentage deviations from steady state (except for the policy rate, which is shown as absolute deviations) and the x-axis represents quarters, extending up to 10 years ahead). The Chart shows that NBFIs significantly amplify the contractionary effects of monetary policy, due to their exposure to bond prices. When bond prices decline, NBFIs cannot offset the reduction in bank credit, meaning they cannot fully fill the gap left by banks. This limitation outweighs the competitive lending channel identified by Gebauer and Mazelis (2023), where NBFIs might otherwise step in to increase credit supply when banks retrench. In our analysis, the balance sheet channel dominates, so the ability of NBFIs to act as a ‘spare tyre’ is significantly curtailed during periods of falling bond prices.

Chart 1: NBFIs amplify the negative effect of higher interest rates on GDP

Importantly, if we run a large number of simulations with randomly drawn shocks to characterise the full distribution of outcomes, we find that this amplification is most pronounced in the left tail of the GDP distribution. To clarify, these charts illustrate the impact of introducing NBFIs on GDP in extreme scenarios. The median value falling by 0.01 percentage points suggests that, on average across all simulations, the effect on GDP is minimal. However, the shift of the fifth percentile by -0.81 percentage points indicates that in the worst 5% of simulated outcomes, GDP is significantly lower − that is, deep downturns become noticeably more severe when NBFIs play a larger role. This heightened vulnerability persists even when interest rates are constrained at the zero lower bound, meaning that the risk of sharper contractions in GDP remains present under stressed conditions.

Table A: Median and 5th percentile values of the distribution of GDP deviations from steady state across 1,000 simulations

In contrast, our long-term analysis indicates that greater involvement of NBFIs supports higher overall welfare. Chart 2 illustrates how aggregate welfare changes as the proportion of NBFI credit rises − in particular, as the NBFIs share increases from 0 to 0.3 and the banks share drops correspondingly. We observe a clear trend: welfare tends to increase as the proportion of NBFI lending rises, with the most pronounced gains occurring when NBFIs are first introduced to an economy − specifically between a share of 0 and 0.1. By facilitating a broader spectrum of lending channels, an increased share of NBFI activity supports a more diverse and adaptable financial system, which can enhance the allocation of resources without relying solely on the regulatory mechanisms applied to commercial banks.

Chart 2: Aggregate welfare as a function of NBFI share of total lending

Policy implications

These findings highlight that while NBFIs may enhance long-term welfare by expanding credit channels and supporting economic efficiency in normal circumstances, their growing presence also renders the financial system more susceptible to severe downturns. In other words, the improvement in welfare during typical economic conditions comes at the cost of increased vulnerability to extreme shocks.

Policymakers must therefore strike a thoughtful balance between stability and efficiency. Adaptive oversight is crucial, because effective macroprudential policies must address risks arising from every part of the financial system − not only by evaluating banks and non-bank institutions individually, but by understanding their interactions and the combined effects these have on the broader economy. This requires a dynamic regulatory framework that considers the evolving interplay between regulation, monetary policy, and the diverse spectrum of financial intermediaries.

In summary, understanding these complex dynamics equips policymakers to better prepare for future shocks and enhance financial system stability and welfare.

Manuel Gloria and Chiara Punzo work in the Bank’s Macroprudential Strategy and Support Division.

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