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On Implementing Macroprudential Policy

Author(s): Itai Agur, Sunil Sharma

Date published: Jun 2022

SUERF Policy Brief, No 362*

by Itai Agur (International Monetary Fund) and Sunil Sharma (George Washington University, Elliot School of International Affairs) 

*The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board, or its management.

Email addresses: iagur@imf.org (Itai Agur) and sunil_sharma@email.gwu.edu (Sunil Sharma). 

JEL codes: G01, G18, G28.
Keywords: macroprudential policy, systemic risk, financial stability, regulation.

Download: SUERF Policy Brief, No 362SUERF Policy Brief, No 362 (0.41 MB)

With a deeper understanding of the sources and interconnections underlying systemic risk, has come the recognition that a wider framework and financial stability toolkit may be needed. But if such toolkits are to be effective, regulators and policy makers must have the ability to use them preemptively before systemic fragility increases to dangerous levels. Given the coordination, flow of information, analysis, and communication required among disparate agencies, macroprudential frameworks have weaknesses that make it hard to implement policy, especially in the face of  divisive politics and a lack of accountability. An approach that limits discretion through the formulation of macroprudential rules is hampered by the difficulties in detecting and measuring systemic risk. Oversight may be best served by having a strong baseline regulatory regime on which a time-varying macro-prudential policy could be added as conditions warrant and permit.
Macroprudential policy frameworks have been developed with the aim of containing systemic risk by dampening the amplitude of financial cycles and inhibiting credit and asset booms before they threaten public and financial sector balance sheets and the economy at large. However, by their very nature, systemic threats are “tail events,” and represent an agglomeration of risks from a variety of channels. Therefore, both gathering adequate data and forming consistent views is likely to be demanding, as it involves disparate sources and agencies.

The origins of systemic risk can be both domestic and external in nature. Vulnerabilities in financial systems often build up with increasing cross-border connections and exposures, and can lead to trouble if markets seize up, capital flows reverse, and balance sheets unwind (Portes et. al. (2020)). In view of this, IMF (2022) lays out a conceptual framework for an integrated perspective on macroprudential policy, capital flow measures, and foreign exchange intervention to discuss conditions under which preemptive measures can form a useful part of the financial stability policy toolkit. However, given the links and interactions of the financial sector with other sectors of the economy, an even broader perspective may be required.

1. Macroprudential policy and systemic risk assessment

Systemic risk is amorphous, arising in unexpected ways that are not necessarily informed by the experience of past crises. This can be due to the non-linearity of effects in a complex, evolving economy (see, for example, Haldane (2012), Bookstaber (2017), White (2018, 2020a)). Some ambiguous thresholds may be crossed, instigating a move away from a seemingly stable path towards a state of crisis. Uncertainty and threshold effects inhibit the proper quantification of systemic risk and hinder the construction of effective early warning systems that could inform preemptive action before risks materialize.

Systemic risk quantification remains in need of a comprehensive operational characterization. While systemic risk metrics have made progress in recent years, they have not yet produced satisfactory measures, despite the variety of models and methods used (Benoit et al. (2017), Engle (2018), Brogi et al., (2021), Capponi and Jarrow (2021)).

2. From measurement challenges to implementation challenges

The nature of systemic risk and the difficulties associated with measuring it influence the conduct of macroprudential policy (Borio (2011), Agur and Sharma (2015), Shin (2015), Stellinga (2020)). Take the construction of early warning systems as an example. Such systems must identify in advance what interventions will be taken when systemic risk rises to critical levels, otherwise policy makers have to defer decisions to when risks materialize and then determine the appropriate course of action. The latter option leaves full discretion in the hands of the regulators and depending on institutional and political structures such discretion could open the door to resistance from the financial industry, politicians, and even the public.

Operationalizing a policy that is both time varying and rules based is likely to be unachievable, due to the difficulty of adequately quantifying systemic risk. An effective rule must stipulate beforehand how policy will react when specific events transpire. However, in the realm of macroprudential policy such events refer to systemic risk crossing prespecified boundaries while the associated actions entail the initiation of macroprudential interventions. In view of the inherent challenges in the quantification and assessment of systemic risk and the design of appropriate macroprudential instruments, attempting to predetermine policy actions for rare events, and properly calibrating such tools based on relatively scant data, is problematic to execute and communicate.

The hurdles faced in the implementation of macroprudential policy can be illustrated by a comparison with monetary policy measures aimed at containing inflation. First, the “event,” inflation in goods and services, is well-defined, as is the “act” of raising short-term interest rates. Further, there is historical experience, data, and reasonably well-founded models that tell us how interest rates affect inflation1. Moreover, the inflation gauge is a relatively simple one, which is readily available and comprehensible to the public. Instead, macroprudential regulation is bound to systemic risk measurement, which cannot be suitably represented by simple indicators. A rule that ties a multitude of indicators to a variety of tools is hard to formulate, calibrate, and communicate. This challenge is exacerbated by the relative paucity of data on past macroprudential actions and their impact on crisis prevention, and the necessity of forming a judgment relative to a counterfactual that relies more on a priori arguments than firm evidence. 

In implementing macroprudential remedies, measurement problems interact with the political economy of policy formulation. If a central bank moves to raise interest rates when it finds that inflationary pressures are building up, there is less scope for a lobby to counter that inflation is not being accurately measured and no incentive for any lobby to do so since no sector is singled out and the policy applies to the public at large. In contrast, as macroprudential policy tightening is based on systemic risk measures that are open to dispute, it allows special interest groups to oppose the policy decisions. Furthermore, it is more difficult to tell only a few of the proverbial partygoers to resist the punch bowl than to take the bowl out of the room. Moreover, the focus on a single sector that is particularly well-funded and powerful is likely to lead to intense pushback.

Given the diversity of economic and institutional contexts in countries comprising the European Union (EU), measurement challenges are even more difficult to surmount as in addition EU policymakers must face the problems posed by the financial trilemma — trade-offs between financial stability, market integration, and national regulatory discretion (Schoenmaker (2011)). This has hampered the evolution of an EU-wide macroprudential policy framework since the use of macroprudential instruments has been constrained by procedural requirements and limits on their intensity and scope. While national authorities must justify discretionary regulatory actions to the EU, it is hard to do so in the absence of agreed norms for measuring and mitigating systemic risk (Stellinga (2021)).

3. Institutional structures and policy implementation are likely to be country and path dependent

Some of the difficulties associated with the implementation may be alleviated by assigning central banks the mandate for macroprudential policy. Such a mandate brings together systemic risk analysis, macroprudential decision making, and communication at the central bank. However, the exclusion of other agencies from the decision-making process has the disadvantage that financial regulators who provide the key information on the health of markets and intermediaries and implement policy are not involved in the macroprudential decisions. Also, the provision of “soft supervisory information” for decision making that may be important and not easy to convey, may suffer in such an arrangement. Legally binding powers to make regulators enact the central bank’s macroprudential decisions are challenging to formulate and this, in turn, risks eroding the credibility of macroprudential decisions and their communication (Agur and Sharma, 2015).

A joint committee where all the agencies have a say could prevent dogmatic thinking but adds to the complexity of decision making. Deliberations among officials with diverse backgrounds and experience should improve the design of policy and such a setup also limits conflict between agencies, enabling better policy. Attaining consensus on policy decisions, however, becomes more challenging and slows down the responsiveness of macroprudential policy to rapidly evolving events and may hamper coherent communication. Furthermore, the greater the diversity of agencies represented on the committee, the more entry points industry lobbies have to affect the committee’s decisions. For example, certain agencies on the committee may not have the requisite budgetary and political independence (Fullenkamp and Sharma (2012)). The demand for inter-agency coordination may require a substantial overhaul of the existing institutions of financial oversight, not to mention a change in regulatory philosophy to manage the transition to a digital economy (see, for example, Omarova (2015, 2018, 2019) in the US context).

Since many of the potential problems in the implementation of macroprudential policy pertain to the interaction between separate agencies, it may seem attractive to unify monetary policy, bank regulation and macroprudential policy within one agency, namely the central bank. However, whether such an institutional setup would indeed make it easier to plan and manage macroprudential interventions depends on a country’s size, history, and the evolution of its political and institutional structures (Edge and Liang (2019)).

The creation of a super-agency with responsibilities for micro- and macroprudential regulation, as well as monetary policy does resolve the problems of inter-agency conflict. But it creates an unwieldy institution with far-reaching powers that is outside the realm of democratic accountability. In democracies, such an institutional design may not be legitimate, or politically and socially acceptable (Tucker (2018), Shirakawa (2021)). Conflicts of interest may also be pervasive in the decision-making process of a super-agency. For instance, where the interests of monetary policy and newly endowed prudential powers collide, central banks may be tempted to give primacy to their longstanding monetary policy objectives. One example is reputational risk: if bank failures harm the reputation of the institution, including its monetary policy credibility, the policy maker may face stronger incentives for regulatory forbearance to prevent the revelation of problems in the banking sector (Agur, 2021). There is some evidence that policy decisions in the realm of bank supervision are affected by monetary policy considerations, when a central bank holds sway over both (Ioannidou, 2005).

Additionally, getting the timing and intensity of macroprudential policy measures correct is particularly challenging due to the complications associated with the identification and quantification of systemic risk, the likely pushback from industry lobbies, and the need to apply such measures preemptively before risks become fully apparent. Given these obstacles, central banks facing complex economic and political tradeoffs may choose to delay decisions. For instance, central banks might defer introducing or escalating macroprudential measures, knowing that emergency lending and liquidity provision could be used to address financial and market turmoil should it arise.

Recent history does not inspire confidence. Macroprudential policy is the dynamic component of a financial stability regime whose purpose is to ensure that concerns about financial stability do not affect the functioning of the real economy. To this end, before assessment and policy formulation, financial authorities must seriously examine whether the baseline static regime – the component of financial stability policy that is not cycle dependent – is complete, clear, and incentive-compatible (White (2020b), Tucker (2021)). Time-varying macroprudential policy cannot counterbalance a weak baseline prudential regime and structural deficiencies in the financial system, and may not be able to preempt the rise in systemic risk (Agur and Sharma (2015), Freixas, Laeven and Peydró (2015)). While banks may have performed better during the COVID-19 pandemic compared to the global financial crisis of 2008, market-based finance had to be bailed out again to prevent a financial system breakdown. Several aspects of nonbank finance, need urgent attention, such as rising credit extension by the shadow banking system based on the creation of short-term money-like instruments, redemption risk in open-ended funds and its interaction with illiquidity on derivatives and securities markets, and the systemic risks posed by the expanding asset management industry (see, for example, Scott (2016), Arner et al. (2019), White (2020b), Wilmarth (2020), Kohn (2021)).

Taming the credit cycle and containing rapid credit growth are key objectives if central banks and regulatory agencies are to preempt the buildup of risks, market distortions, and inequality (White (2020b), Petrou (2021), Leonard (2022)). Recent crises have not been precipitated by rampant inflation, but by rising debt and resulting fragilities in financial intermediaries and markets, corporations, households, and governments. Time-varying macroprudential policy must negotiate  the financial cycle and its interactions with the business and electoral cycles. To do this effectively, macroprudential, monetary, and fiscal policies will have to be employed in tandem so that they push in the same direction and are able to do so before the risks materialize (White (2019)).

4. Defining and evolving a panoptic perspective?

The difficulties of calibrating and implementing macroprudential policy are amplified when the systemic fragilities extend beyond the financial system. The financial sector is an intermediary in a complex evolving economy, and its health must be evaluated in conjunction with the health of the broader economy and the natural environment on which it surely depends (Florini and Sharma (2020), White (2020c), Alogoskoufis et al. (2021), Engle, Berner, and Jung (2021), Chenet et. al. (2021, 2022)). More generally, for extended periods of time, macro-stabilization and regulatory policies cannot be used as a substitute for addressing deep structural problems in financial, economic, and political systems (Sharma and White (2022)).

Systemic thinking will have to encompass the entire economy and the environment. An integrated perspective will have implications for the conduct of social, economic, and financial policies, including macroprudential policy, and the politics of democratic decision-making. Such an approach by its very nature requires a wider set of policy tools, action by many agencies at various levels of government, an international dimension, and faces more profound challenges in institutional design, operation, coordination, and public communication.

Today, financial technologies, new electronic payment systems, and the feasibility of introducing central bank digital currencies offer a singular opportunity for societies to re-examine fundamentally the nature of money, how it is created and distributed, and shape the institutional structure and functioning of the financial system and its regulation to produce greater systemic stability, efficiency, and equality (Gnan and Masciandaro (2018), Mancini-Griffoli et al. (2019), Auer et. al. (2022), Kosse and Mattei (2022)). How the system develops could transform the conduct of economic and financial policies and the institutional structure of surveillance and regulation.


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About the authors

Itai Agur is a Senior Economist at the IMF’s Research Department (Macro-Financial Division). Previously, he worked at the IMF’s Strategy, Policy and Review Department (2015-2017), where he joined IMF missions to Canada and Mongolia, and the IMF’s Singapore Regional Training Institute (2011-2015). Prior to joining the IMF, he worked at the Research Department of the Dutch Central Bank (2008-2011) and the European Central Bank (2003-2004). His research, published in both academic and policy outlets, centers on macro-financial interactions, macroprudential policy, and fintech. His work has earned him the European Economic Association’s Young Economist Award and the Arrow Award for Junior Economists.

Sunil Sharma is a Distinguished Visiting Scholar at the Institute for International Economic Policy, Elliott School of International Affairs, The George Washington University, Washington DC, USA, and a Senior Associate at the Council on Economic Policies, Zurich, Switzerland. He was Assistant Director in the IMF’s Research Department from 2015-2018, and the Director of the IMF-Singapore Regional Training Institute (STI) in Singapore from 2006-2015. Before moving to Singapore in 2006, Sunil was Chief of the IMF Institute’s Asian Division in Washington, D.C. Prior to joining the IMF in 1992, he was on the Economics faculty at the University of California, Los Angeles (UCLA). https://iiep.gwu.edu/sunil-sharma/

1.     It is worth noting that recently inflation models have also not held up well.

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