Eduardo Maqui, Nicholas Vause and Márcia Silva-Pereira

In recent decades, the corporate bond market has grown from a relatively niche source of finance for UK corporations to a central pillar alongside bank loans. This transition raises an important question: as with bank credit conditions, have supply conditions in the corporate bond market come to significantly affect UK economic activity? Our recent research suggests the answer is a resounding yes. We show that a measure of corporate bond financing conditions − the Excess Bond Premium (EBP) − not only anticipates macroeconomic outturns in the UK, but also influences investment by UK firms, especially those that are highly leveraged and more reliant on bond finance.

The rise of bond financing

To motivate our analysis, Chart 1 shows how the composition of UK corporate debt has changed over the past 35 years. A key feature is the rising share of debt securities (mainly corporate bonds), which increased from just 15% in the early 1990s to over 40% by the mid-2020s. Indeed, UK corporations now raise as much finance from bonds as bank loans.

Chart 1: Composition of UK corporate debt

Notes: Non-bank loans includes finance leasing and peer-to-peer lending as well as direct and syndicated loans from non-bank financial institutions. Debt securities is mainly (>90%) corporate bonds but also includes commercial paper.

Source: Bank of England calculations.

Measuring bond financing conditions: the excess bond premium

To study how financing conditions in the corporate bond market affect economic activity, we first require a summary measure of those conditions. Thus, we follow Gilchrist and Zakrajšek (2012) by decomposing corporate bond spreads − the additional compensation required by investors to buy corporate bonds rather than government bonds − into two components. One component reflects ‘fundamentals’ relating to the riskiness of the borrowers or the specific bonds. The residual component is known as the EBP and reflects risk appetite of investors. Specifically, when the EBP rises, it signals that investors require more compensation to hold corporate bonds, over and above what is justified by borrower default risk or bond-specific risks such as illiquidity.

We compute the EBP for the UK by identifying the bond obligations of individual UK firms over time, taking into account mergers and acquisitions. We then combine various sources of data on these matched firms and bonds in order to regress corporate bond spreads on measures of obligor-specific default risk (in particular the distance to default) and bond-specific market and liquidity risks (such as modified duration and size of issue). We retain the residuals from this regression and aggregate them across firms to form the EBP.    

Chart 2 shows our results. Investor willingness to invest in UK corporate bonds at lower rates of compensation generated a negative EBP for much of the decade preceding the 2007−08 global financial crisis (GFC) − a period of low macroeconomic uncertainty (The Great Moderation). The EBP then swung sharply positive during the GFC, when investors required substantially more compensation to invest in bonds than suggested by fundamentals. The EBP was also distinctly positive in other periods of financial stress or economic uncertainty, in particular following the dot-com crash (2000−01), during the euro-area sovereign debt crisis (2010−12), ahead of the Brexit referendum (2016), at the outbreak of the Covid-19 pandemic (2020), and following the Russian invasion of Ukraine (2022).

Chart 2: Decomposition of UK corporate bond spreads

Notes: The chart shows an index of corporate bond spreads constructed from 1,680 bonds issued by 149 UK private non-financial corporations (black line) and how it decomposes into a component explained by borrower and bond-specific fundamentals (dark blue) and the excess bond premium (light blue).

Source: Authors calculations. Based on Gilchrist and Zakrajšek (2012).

What happens when bond financing conditions tighten?

Equipped with our measure of bond financing conditions, we first study the consequences of changes in conditions for macroeconomic indicators, including GDP, investment and the unemployment rate. We take two approaches. First, we employ local projections, regressing changes in the macroeconomic indicators from 1 to 16 quarters ahead on contemporary changes in the EBP. In these regressions, we include the policy interest rate and the term spread, as well as several other control variables, to isolate changes in the macroeconomic indicators already anticipated by these other predictors. As shown in Chart 3, a one standard deviation increase in the EBP (of 53 basis points) is associated with a decline in GDP of as much as 2 percentage points, a reduction in investment of as much as 4 percentage points, and an increase in the unemployment rate of as much as 0.5 percentage points. These peak effects all occur about 1.5 years after the shock.

Chart 3: Impulse response of macroeconomic outcomes to an EBP shock

Notes: The panels show estimates of the effects of a one standard deviation EBP shock on macroeconomic outcomes up to four years after the shock. The solid lines show the expected effects, while the darker and lighter shaded areas respectively show ranges in which we are 90% and 95% confident that the effects lie. Refer to the staff working paper for details of the methodology. Investment is gross fixed capital formation.

Source: Authors calculations. Based on Gilchrist and Zakrajšek (2012).

While these effects are sizeable, note that the estimates come with a significant range of uncertainty (blue-shaded areas of the chart). They also depend on our assumption of being able to infer shocks to bond financing conditions from changes in the EBP, which may to an extent be confounded by other macroeconomic drivers. As a sensitivity check, we compute impulse response functions based on a vector auto-regression model and find weaker responses, of around half the magnitudes reported above (refer to Appendix D in the paper), although these effects remain economically significant. Potential limitations to our identification should bite less at the firm level, which we explore below, since firm-level outcomes are less likely to be correlated with confounding aggregate dynamics.

Digging into these aggregate economic responses with similar analyses at sector level, we find that the impact of changes in the EBP is not uniform across different parts of the economy. Notably, investment in capital-intensive assets − like machinery, equipment, and buildings − declines much more than investment in intellectual property. Similarly, investment in manufacturing and production industries is hit harder than investment in services. Interestingly, public-sector investment tends to move countercyclically, increasing when private investment falls, which helps to stabilise capital formation in aggregate.

Firm-level effects: who gets hit hardest?

Finally, we study the effects of shocks to bond financing conditions, as captured by changes in the EBP, on individual firms. Here, we allow for different responses depending on both the level and composition of firms’ debt. Specifically, we allow for different responses for firms in each of the four groups shown in Table A. We estimate these responses through separate local projections for each group, where we regress firm-level outturns − such as growth in investment, assets, sales and profits − over various future horizons on contemporaneous changes in the EBP.

Table A: Firm groups by leverage and share of bond financing

Chart 4 shows the results for investment, which is one of our key findings. While the first three panels show no statistically significant response of investment − as measured by capital expenditure − by LL, LH or HL firms to changes in the EBP; the final panel shows that HH firms cut investment aggressively, with a peak decline in investment of almost 10 percentage points around 1.5 years after a one standard deviation shock. Hence, it appears to be the behaviour of these firms − which are not only highly leveraged but have a high share of bonds in their debt − that drives the response of aggregate investment (shown in Chart 3).

Chart 4: Impulse response of firm-level investment to an EBP shock

Notes: The panels show estimates of the effects of a one standard deviation EBP shock on the capital expenditure of UK firms up to four years after the shock for firms with low leverage and low bond share (LL), high leverage and low bond share (HL), low leverage and high bond share (LH) and high leverage and high bond share (HH). The solid lines show the expected effects, while the darker and lighter shaded areas respectively show ranges in which we are 90% and 95% confident that the effects lie. Refer to the staff working paper for details of the methodology.

Source: Authors calculations.

This evidence is consistent with a financial accelerator mechanism in which highly leveraged firms cut investment especially sharply when the cost of finance increases, thereby amplifying the sensitivity of aggregate investment to changes in EBP compared to an economy with a more-even distribution of debt. Our results add a new dimension to this mechanism, as we show the amplification of the investment response to changes in the EBP depends not only on a firm’s leverage but also on the share of bonds in its debt. The results therefore characterise a specifically market‑based finance propagation channel, in which the structure of corporate debt shapes the transmission of financing shocks to real economic activity.

Why does this matter for policy?

Our findings have several important implications. First, the EBP provides a timely signal of changes in bond financing conditions that can foreshadow changes in economic activity. Hence, it may serve policymakers as a useful complement to other business-cycle indicators. Second, the amplified response to changes in the EBP for highly leveraged, bond-reliant firms highlights the importance of diversified funding sources for economic resilience. Third, having shown how changes in bond financing conditions ripple through investment, employment and growth, future research on what in turn determines these conditions seems particularly valuable.

Eduardo Maqui works in the Bank’s RegTech, Data and Innovation Division, Nicholas Vause works in the Bank’s Market-Based Finance Division and Márcia Silva-Pereira is an Economist at Banco de Portugal.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Manuel Gloria and Chiara Punzo

The expansion of non-bank financial institutions (NBFIs) is transforming the financial landscape and introducing fresh challenges for financial stability and oversight at the same time as creating opportunities. Using a dynamic stochastic general equilibrium (DSGE) model, we find that while NBFIs may enhance long-term welfare for households and entrepreneurs in normal conditions, their greater role also heightens vulnerabilities to severe shocks in the financial system. Greater NBFI activity boosts competition in the financial sector, leading to more efficient resource allocation. A working paper detailing these results was recently published.

Introduction

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.

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

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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.

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

GDP	No NBFI	NBFI	Diff
Median	-0.17%	-0.18%	-0.01%
5th percentile	-9.17%	-9.98%	-0.81%
Median (zlb)	-0.36%	-0.39%	-0.03%
5th percentile (zlb)	-9.07%	-9.87%	-0.80%

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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.

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

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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.

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

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

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