Financial Innovation and Statistical Methodological Guidance—Key Considerations

Joe Crowley; Marco A Espinosa-Vega; Elizabeth Holmquist; Ken Lamar; Emmanuel Manolikakis; James McAndrews; Holt Williamson

doi:10.5089/9798400224157.001.A001

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Financial Innovation and Statistical Methodological Guidance—Key Considerations

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Mr. Joe Crowley

Mr. Joe Crowley
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Marco A Espinosa-Vega

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https://isni.org/isni/0000000404811396 International Monetary Fund

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Elizabeth Holmquist

Elizabeth Holmquist
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Ken Lamar

Ken Lamar
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Emmanuel Manolikakis

Emmanuel Manolikakis
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James McAndrews

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https://isni.org/isni/0000000404811396 International Monetary Fund

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Holt Williamson

Holt Williamson
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Publication Date:: 28 Oct 2022

eISBN:: 9798400224157

ISBN:: 9798400224157

Language:: English

Keywords:: NBFI subsector; Non-bank Payment Service Providers; challenger bank; demand deposit; medium of exchange; Nonbank financial institutions; Global

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Financial risks outside of the traditional banking sector can quickly spread throughout financial systems and lead to disruptions in the real economy. A lack of adequately detailed financial sector statistics can obscure buildups of risks from policymakers and hinder their ability to effectively respond once these risks materialize. In response, authorities worldwide, international organizations, including the IMF, and the Group of 20 (G-20), called for financial reforms and launched efforts to gather information on nonbank financial intermediary (NBFI) activities—including the Data Gaps Initiative (DGI) and enhanced Financial Stability Board (FSB) NBFI data collection. While these initiatives represent significant strides to strengthen NBFI’s data collection, there continue to be gaps in the conceptual and methodological guidance in the financial and macroeconomic statistics manuals on which the FSB, DGI, and national authorities rely; gaps that are increasing in light of increased globalization and the financial sector digitalization. This paper proposes conceptual guidance to help bridge existing and emerging gaps.

Abstract

I. Introduction

As the Global Financial Crisis (GFC) made clear, financial risks outside of the traditional banking sector can quickly spread throughout financial systems and lead to disruptions in the real economy. Prior to the GFC, for example, rapid growth in credit intermediation via the so-called shadow banking system (e.g., securitization) received relatively little attention from regulators and policymakers. This neglect stemmed, in part, from a lack of adequately detailed statistics, including on the size and interconnectedness of emerging risk exposures.

In response, authorities worldwide, international organizations, including the IMF, and the Group of 20 (G-20), called for financial reforms and launched efforts to gather information on nonbank financial intermediaries’ (NBFI) activities. A well-known outcome was the G-20 Data Gaps Initiative (DGI-I) geared towards strengthening relevant data collection for financial stability analysis (Box 1).

^{Box 1.}

The Data Gaps Initiative (DGI): Goals and Achievements

In the wake of the GFC, the G-20 Finance Ministers and Central Bank Governors Working Group on Reinforcing International Co-operation and Promoting Integrity in Financial Markets (G20WG) called on the IMF and FSB to identify data gaps—including those related to complex instruments, off-balance sheet entities, and cross border linkages of financial institutions that hindered policy responses during the GFC—and to recommend ways to strengthen relevant data collection for financial stability analysis. The DGI had two stages.

During the first stage (DGI-1), the G20WG made a list of 20 key recommendations covering three main areas relevant for financial sector surveillance.

Monitoring risk in the financial sector including Financial Soundness Indicators (FSI) country coverage; standardized measures of tail and other distributional risks; system-wide macroprudential risk; credit default swaps (CDS) markets; disclosure requirements for complex products; and information on securities.
Tracking international network connections including linkages across individual financial institutions with special attention paid to GSIBs; CPIS and IBS; identification of nonbank financial counterparts, perhaps including for nonresident exposures; IIP; cross border flows and exposures (financial and nonfinancial); and a standardized template for international exposures of large NBFIs.
Expanding sectoral and other financial and economic datasets including balance sheets, flow of funds, and sectoral data; as well as nonfinancial sector information, including distributional data (i.e., income quartiles) Government Finance Statistics (GFS), public sector debt; and real estate prices.

At the completion of the DGI-1, in 2015, only a handful of the recommendations had been fully completed.

At the same time, the DGI-2 was launched to complete the DGI-1 recommendations and to implement new recommendations on derivatives and the Coordinated Direct Investment Survey (CDIS). When the DGI-2 was completed in 2022, about half of the recommendations had been fully completed and significant progress had been made on the rest.¹

The recommendations that had not been fully completed by the end of the DGI-2 include:

Recommendation 5) Shadow banking. Most G-20 countries made limited progress on collecting and disseminating securities financing transaction data (loans securitized with securities, including repos).
Recommendation 10) IIP. Gaps relate to other financial corporations (OFCs) and currency composition.
Recommendation 11) IBS. Several countries do not report consolidated banking statistics, which measure banks’ country risk exposures on a worldwide consolidated basis.
Recommendation 14) Cross border NBFI data. Only thirteen G-20 countries reported NBFI data, and this is done via IMF’s Standardized Report Forms for OFCs (SRF-4SR), ECB, and one other method, raising comparability questions.

1 See closing See closing DGI-1 report https://data.imf.org/api/document/download?key=60952980 and DGI-2 report https://www.imf.org/-/media/Files/Research/imf-and-g20/2022/g20-data-gaps-initiative-dgi-2-closing-report.ashx.

In line with these efforts, the Financial Stability Board (FSB) initiated an annual global collection of macro data on NBFI, which in its broadest measure includes all private, non-deposit-taking financial institutions.¹ Additionally, the FSB also tracks two subsets of NBFIs: Other Financial Intermediaries (OFIs) and a narrow measure of NBFIs, which encompasses “entities that may pose bank-like financial stability risks (i.e., credit intermediation that involves maturity/liquidity transformation, leverage transformation, leverage or imperfect credit risk transfer).”² Over the years, ambitions for this data collection’s coverage of NBFI institutions, activities, and interconnections has continued to expand in granularity and scope—due at least in part to digital financial services innovations and the increasing demand by stakeholders for additional clarification of how this trend may impact the economy.

While these initiatives represent significant strides to strengthen NBFI’s data collection, there continues to be gaps and, more broadly, a need for additional conceptual and methodological guidance in the financial and macroeconomic statistics manuals, on which the FSB and the DGI rely. This paper proposes guidance to help bridge these existing gaps.³

The paper starts with an overview of the current NBFI taxonomy within the other financial corporations (OFC) sector) as well as select financial instruments in the financial and macroeconomic statistics manuals (FMSMs).⁴ It identifies areas where the taxonomy would benefit from updates, clarification, streamlining, and consistency. Furthermore, it suggests expanded coverage of the NBFI sector to capture potential financial stability risks. Finally, it compares the proposed new breakdowns with those planned in the context of the SNA and Balance of Payments Manual updates and proposes a common core taxonomy.

The paper proceeds to review recent developments in Fintech and illustrates how economic theory, funding sources, risks, and the scope of regulation need to be considered for their classification in official statistics. Finally, the paper proposes possible paths forward for conceptual guidance updates. This version of the paper refrains from a number of topics, including cross-border discussions—to be better addressed in a follow up version.

II. Non-Bank Financial Intermediation in Macro Statistics

International macroeconomic statistical standards are periodically revised to ensure they reflect the current structure of the economy and financial systems. The latest update process for the BPM6 and 2008 SNA was launched in 2020 with a target publication date of 2025. Some of the issues being addressed in the update of the manuals center on the treatment of NBFI subsectors and instruments. Box 2 provides an overview of the main recommendations of these guidance notes.

^{Box 2.}

Guidance Notes Related to Non-Bank Financial Intermediation and Fintech Recording

The Joint Financial and Payments Systems Task Team (FITT) drafted a series of guidance notes concerning updates to the BPM6 and the 2008 SNA with recommendations, some of which have already been approved by both the IMF Committee on Balance of Payments Statistics, and the Advisory Expert Group on National Accounts. The notes’ related to non-bank financial intermediation and the recording of fintech activities are summarized as follows

The guidance note on “Capturing Nonbank Financial Intermediation” and a follow up note on “More Disaggregated Institutional Sector and Financial Instrument Breakdowns” recommend additional sectoral and instrument breakdowns in SNA and BPM to better capture non-bank financial intermediation.

Additions to the SNA sectoral breakdowns would include dividing money market funds (MMFs) into constant and variable net asset values (NAV), separating insurance into life and non-life corporations, and separating pension funds into defined benefit and defined contributions plans (a third note addresses hybrid insurance and pension products). Furthermore, detailed subcategories would be added to non-MMF investment funds (12), OFIs (6), and captive financial institutions and money lenders (4). These additional categories are reflected in Appendix IV.

Additional subcategories for the BPM would include MMFs, non-MMF investment funds, insurance corporations, pension funds, OFIs (of which central clearing counterparties), and captive financial institutions and money lenders, and financial auxiliaries. Furthermore, “Loans” in the updated SNA and BPM would include an “of which” line for repos, and in external sector statistics (ESS), nonfinancial corporations would be separated from households and NPISHs.

The guidance note “The Recording of Crypto Assets in Macroeconomic Statistics” examines the classification of crypto assets / currencies. Key issues being reviewed include whether crypto assets without a corresponding liability should be classified as non-financial assets or as financial assets.

The guidance note on the “Impact of Fintech on Macroeconomic Statistics” finds that while the concept of “fintech” is not specifically referenced in the existing international statistical standards, they allow for the proper treatment and recording of fintech companies and fintech related activities in most cases without the need to introduce a new “fintech” sector. However, it proposes that an “of which fintech” category can be included within the existing activity and sector classifications for countries with significant fintech activities (not reflected in Appendix IV).

The guidance note on “Financial Derivatives” recommends key changes for classifying derivatives. The main change would be for derivatives to be broken down by market risk category (6), instrument (7), and trading venue and clearing status (3) (market risk and instrument breakdowns are included in Appendix IV). Other items include clarifications and reemphasis of existing guidance.

The guidance note on “Credit Default Swaps” (CDSs) recommends continuing to classify CDSs as options instead of forward contracts. The reasoning is that CDSs share several characteristics with other options, while the most notable characteristic shared with forward contracts is that its market value may switch from positive (asset position) to negative (liability position) or vice versa over the contract period. It also recommends breakdowns of financial derivatives by risk category so that credit derivatives can be separately identified.

The guidance note on “Reverse Transactions” (RTs) recommends maintaining the current recording of RTs in BPM6 and 2008 SNA. It also recommends several clarifications (recording of short RT positions, interest and dividends on borrowed securities, identify borrower counterparties, etc.) and discussing separate presentations of RTs in in the updated SNA and BPM.

These guidance notes provide important conceptual clarifications about the recording of NBFI and digital assets in the official accounts. This paper elaborates on the need for further updated guidance and argues that a common taxonomy should be incorporated across the FMSM. In doing so, it outlines the financial stability rationale for additional NBFI breakdowns, a broader analysis of digital financial intermediation, and additional recommendations to continue to strengthen FMSM guidance for the development of financial intermediation statistics to aid macro stability analysis.

The State of Play

Financial and macroeconomic statistical manuals play an important role in the conceptual and methodological guidance for the production of relevant NBFI statistics. For example, the FSB’s annual Global Monitoring Report on NBFI draws from sectoral balance sheet information provided by national authorities. Compilers are referred to 2008 SNA and related manuals for guidance when completing the reporting template on non-bank financial intermediation.

However, the detail needed to move from a measure of other financial intermediaries to economic funds, as requested by the template, goes far beyond the nine financial corporation subsectors described in the 2008 SNA, and thus reporters have found the existing SNA taxonomy insufficient.

This is not surprising considering how the financial sector has evolved since the drafting of the SNA. While many of the financial subsector details now being requested for financial stability monitoring by the FSB will be addressed by enhancements to the SNA that are planned for the upcoming 2025 update (for example, detail on equity, bond, and hedge funds), there will continue to be gaps—such as for the new FSB request for government MMFs statistics.

Additionally, in some places current FMSM guidance may be inconsistent or non-existent. For example, the classification of guidance for real estate investment trusts (REITs) and real estate funds—an important link between the financial sector and the real economy—is complex and ambiguous.⁶ A compiler providing information to the FSB template that calls for a breakdown between REITS and real estate investment funds, and further—between equity and mortgage types as part of the financial corporations sector—will be left confused when they are told to rely on the national accounts for guidance. This lack of clarity could ultimately result in reduced international comparability for the FSB collection and measurement errors in key macroeconomic indicators such as saving, investment and equity in the sectoral accounts with potential misleading policy guidance by decision-makers.⁷

In this case, as in numerous others, there is a need to update and expand the guidance in the SNA and BPM manuals, as well as to ensure that the methodology is consistent across all official manuals. The aim should be to provide a common core taxonomy across all official manuals, augmented with additional domain specific material as deemed relevant.⁸

Currently, there exist differences in the way NBFI activity is defined, classified and presented in the various FMSMs. The updating of the BPM and SNA and future update of the GFS and MFSM provide an excellent opportunity to improve the consistency with which NBFI statistics are produced and presented to users. Given the importance of an up-to date conceptual guidance for the collection of relevant NBFI data, Appendix I provides a side-by-side comparison of the current taxonomy across all the main NBFI categories in STA’s suite of manuals for institutional investors and asset managers; market intermediaries; and financial market infrastructure. This bird’s eye view reveals overlaps and differences in taxonomy across STA’s suite of manuals and aids with the identification of gaps and areas in need of update and/or streamlining—to improve analytical efficacy.

Figure 1 below provides an excerpt of the full color-coded authors’ assessment where the current manuals’ NBFI taxonomy could be enhanced (the complete list can be found in Appendix II).

Green cells indicate cases where all core elements of comprehensive taxonomy are there, although the text would benefit from updates (for example, the definition of open-end non-MMF investment funds in the 2008 SNA would benefit from streamlining).
Blue cells indicate cases that are similar to green, however the taxonomy has detail in addition to the core elements that may be removed if not specifically domain-relevant (for instance, the Handbook on Security Statistics’ (HSS) description of open-end funds includes the most popular types of these funds.
Yellow cells refer to cases where the current taxonomy would benefit from clarification. For example, the MFSMCG says that the benefits received by participants in defined contribution (DC) pension plans are “based on the participant’s contributions to the pension fund and the investment performance of the fund” (3.200). However, this statement should clarify that employers may also contribute to a participant’s DC plan.
Red cells indicate where there are inconsistencies across manuals. An example of such an inconsistency is in the BPM description of closed end funds, which indicates that they are also known as “exchange-traded funds” (ETFs). However, most ETFs are open-end funds.
Gray cells indicate no existing definition. While it can be argued that not all manuals require such detailed definitions of each NBFI subsector and instrument for the compilation of their domain’s statistics, as is the case of open end and closed end funds in the FSI and GFS manuals, maintaining common definitions across the suite of manuals may aid compilers understanding and data collection efforts.

For this review, we examined 31 different sector or instrument categories across the 6 manuals for a total of 186 elements (of which 24 green, 28 blue, 44, yellow, 7 red, and 83 missing).⁹ While inconsistencies are rare, these indicate likely priority areas for attention. These categories include the taxonomy of closed-end funds, exchange-traded funds, and REITs (previously discussed), as well as the difference between a foreign currency swap and a cross-currency interest-rate swap. The MFSMCG covered the most of our selected elements while the GFS covered the least.

The Need for Additional Granularity

To provide a clearer window into financial stability risks, we propose that enhancements to the existing taxonomy include additional granularity for institutional investors and asset managers, market intermediaries, and financial market infrastructures.

For example, in many parts of the world, MMFs have become a key part of the financial market plumbing when it comes to the intermediation of short-term sovereign securities and corporate debt—a liquidity transformation that exposes them to runs. Yet, current and the proposed SNA and BPM MMF breakdowns would not allow us to track detailed MMF developments.

Since the GFC there have been several episodes where NBFIs have been at the center of distress in financial markets, such as the March 2020 dash-for-cash (with large redemptions in prime MMFs). It is widely acknowledged today that NBFIs can be important sources of financial stability risk via several channels (e.g., leverage and funding reversals due to the procyclicality of credit). And the type of NBFI that has been at the center of these episodes has included money market funds, hedge funds, broker-dealers, and even traditionally conservative institutional investors such as pension funds and life insurance companies.¹⁰ Measures to mitigate these runs (e.g., allowing stable NAVs only for MMF investing in government securities) introduced in the wake of the GFC have so far been inadequate. Several initiatives, including additional redemption gates, liquidity fees, and swing prices are under consideration.

The DGI and guidance notes in the context of the BPM and SNA updates have already recommended adding NBFI subsectors to the existing main financial sectors listed in Appendix I, as well as some additional detail on particular financial instruments. For example, it is currently recommended to report constant net asset value (NAV) MMFs separately from variable NAV MMFs. This paper suggests compilers consider also capturing data on government and prime MMFs separately for both stable and variable NAV; detail that would have been helpful for the tracking of events such as the March 2020 dash-for-cash.

Recommendations for Taxonomy Updates and Additional Granularity

This paper recommends that the taxonomy related to NBFIs be reviewed and updated across the suite of manuals to enhance clarity and consistency. This will reduce confusion and ultimately improve the availability and quality of statistics. Similarly, the paper suggests additional granularity, even beyond the planned SNA and BPM update, in the conceptual guidance for the collection of NBFI data for macro-financial surveillance. Such guidance will aid national compilers who face continuous demands for statistics with increased granularity and scope – such as with the FSB collection on nonbank financial intermediation– but have little methodological guidance on which to rely.

As illustrated for the case of MMFs, where liquidity mismatches pose a potential systemic risk, the ultimate selection criteria for the proposed NBFI taxonomy and granularity updates would be the type of systemic risk posed by the sector/financial market. Another example is the case of exchange traded funds (ETFs) which have seen significant growth since their inception in the late 90s (Figure 2). ETFs issue shares that are designed to track an underlying passive investment (indexes) and are often promoted as a cost-effective way for investors to gain exposure to long term assets. ETFs hold portfolios of securities financed with the issuance of shares that can be traded continuously on centralized exchanges but can only be redeemed by Authorized Participants (APs)—buying or redeeming shares to arbitrage away any deviations in the ETF price relative to its NAV. ETFs have grown significantly since the turn of the century, holding close to $ 10 trillion in assets.¹¹ These funds have become competitors to MMFs and have expanded beyond holding safe assets. The ETF model relies on authorized participants to act to keep the ETF price on its NAV. However, the liquidity of the underlying asset markets, and liquidity constraints faced by authorized participants has led to share prices deviating from fair market value of the underlying assets during stressful market conditions, which can be destabilizing for institutions that rely on ETFs for cash management.¹² Recent stress events include corporate bond ETFs trading at steep discounts to underlying asset values in March 2020.

Appendix III details the potential financial stability risk triggers associated with the suggested breakdown of NBFI subsectors. We seek to identify key financial subsectors whose business models make them susceptible to risks, such as maturity, liquidity, funding, exchange rate mismatches; excessive leverage, whereby a financial institution may have difficulty absorbing even moderate losses; and counterparty risks, such as credit risk.¹³ This appendix also presents suggested taxonomy for each of the proposed subsectors.

Appendix IV summarizes the list of additional NBFI breakdowns proposed in this paper, relative to the new proposed financial sector breakdowns in the context of the SNA and BPM updates. These include additional breakdowns in MMFs, non-MMFs, REITs, hedge funds, and derivatives. While not all these NBFI breakdowns may be relevant to all jurisdictions, each is tied to specific risks.

In most jurisdictions sponsorship of NBFIs continue to be the norm and the growth of NBFI activities has outpaced that of the traditional banking sector. Within the banking sector, reliance continues to shift to wholesale funding from retail deposits. Some of the suggested breakdowns in appendix would allow for better tracking of core and non-core funding in the banking system, as illustrated in Box 3.

^{Box 3.}

NBFI Data for Financial Stability: Two Examples

Figure 3 illustrates how existing monetary and financial sector data can usefully be deployed in tracking the composition of NBFI liabilities, which can provide insights into the stage of the financial cycle and the buildup of risk. The figure shows that prior to 2008, noncore funding of the financial sector increased at a much faster pace than core funding, and that it was the noncore funded part of the financial sector that suffered by far the greatest contraction when the crisis occurred.

Figure 4 provides a more granular look at specific elements of the NBFI sector. It shows that repos, federal funds, and to a lesser extent MMFs and uninsured deposits, increased sharply prior to 2008, representing a buildup of risk. These riskier markets also suffered greater contractions after the crisis in 20008. Repos were affected in part because of higher margin requirements. Financial intermediaries such as brokers and dealers that rely on the continuous rollover of repo for funding (including Lehman Brothers) were severely impacted. MMFs, normally viewed as safe investments because of their conservative asset portfolios, suffered runs because even small movements in safe assets caused their ability to honor their strict constant NAV obligations to come into question.

The additional breakdowns proposed in this paper would improve the collection of information on the size, composition, and maturity of funding of runnable liabilities. This additional granularity would enhance understanding of the increasingly important role NBFI’s play in the financial sector and provide policymakers with a better view of emerging risks and developments to be addressed.

III. Fintech in Financial and Macroeconomic Statistics Manuals

Currently, guidance for the collection of information on fintech activities in official statistics is limited because the conceptual framework of the 2008 SNA and related official statistics was developed before the rapid growth of digital and financial innovation. This paper, along with the SNA and BPM update and the forthcoming new data gaps initiative (DGI) workplan, proposes steps to address this gap.

Fintech can be defined as technology-enabled innovation in financial services that could result in new business models, applications, processes, or products with an associated material effect on the provision of financial services.¹⁴ Fintech has disrupted payments, credit, wealth management and the transmission of monetary policy. Current fintech examples include payments and “peer-to-peer” lending facilitated by electronic platforms, robo-advisors, the creation of crypto assets, the organization of crypto exchanges, the emergence of stablecoins, loan origination, and many other banking-related services—notably those centered on payments.

To start, we will review fintech payment providers by focusing on nonbank providers of payment services, drawing from Ehrentraud et al. (2021) and McAndrews and Menand (2020).¹⁵

Non-bank payment service providers

Fintech payment providers, or Non-bank Payment Service Providers (NBPSPs) fall into several types, depending on the role they play in the payment landscape. The main distinction among NBPSPs for the purposes of potential data gaps for NBFI statistics is whether the NBPSP 1) directly offers storage of value or 2) whether they rely on third parties for this storage.¹⁶

In the U.S., for example, many of these NBPSPs are regulated as Money Services Businesses (MSBs)— money transmitters. Those that provide storage of value act directly as payment providers and conduct business in a manner very similar to a depository institution. Classic-type MSBs, such as Western Union, issue only specific claims (for example, by depositing money with such a business, one receives a liability that can be redeemed only by a specific person and at specific place). Modern-type MSBs, such as mobile network operators (MNOs, e.g., Venmo, Alipay, M-Pesa) provide a more general-purpose liability that is quite similar to a demand deposit.

Firms such as WeChat pay, Alipay, Venmo, Revolut, M-Pesa, and others have grown rapidly in the last decade to provide online and mobile payments while also offering storage of value in accounts or on mobile devices.

An important question is whether the assets of the NBPSPs that offer storage of value are captured in current monetary statistics collections. The answer will depend, in part, on whether regulation requires these NBPSPs to store their assets with depository corporations (e.g., central banks and commercial banks), and in part on the compiler’s care in capturing electronic money balances as part of the monetary aggregates, as explained in Shirono et al. (2021).

For NBPSPs that offer storage of value in accounts or on devices, most (but not all) countries they operate in, require them to deposit their holdings at a regulated depository institution. They are therefore captured in the broad money aggregates. However, the capture of the payment transactions themselves varies by jurisdiction, and type of regulation (for an analysis of mobile money, see Shirono et al. (2021)).

In some countries, the collection of NBPSPs’ data is complicated by the heterogeneous regulation to which they are subject. NBPSP liabilities are like demand deposits, but because NBPSPs are not regulated as banks they may hold assets outside the banking sector. This may lead to limited coverage or coverage being available only at the subnational level. In the U.S., for example, any statistics on the volume of services are only available at the state level, which provides inadequate statistical coverage for monetary aggregates.

A second set of concerns reflects financial stability. Nonbank regulation does not require non-banks, particularly NBPSPs, to continually keep a minimum stock of liquid assets on hand to redeem liabilities on demand. Maturity mismatches may therefore arise in the balance sheets of NBPSPs. Furthermore, the liabilities of MSBs and other NBPSPs are not insured. Consequently, they represent a risk of failure in the event of a run by their liability-holders. These concerns are greatly attenuated for NBPSPs that provide only front-end communication services and do not store value.

More generally, statistical manuals should create a basic taxonomy of NBPSPs based on their funding sources and the regulatory regimes to which they are subject. For example, for NBPSPs involved in payment processing, consideration will have to be given to whether the NBPSP stores value directly on its balance sheet to support its monetary liabilities or whether it provides access to the monetary liabilities of another financial institution. Regarding NBPSP lenders, attention will have to be paid to whether the NBPSP lends (at least in part) from its own balance sheet, through the issuance of asset-backed securities, or whether it acts as a passthrough, facilitating access to lending by third-party financial institutions. In the latter case, those NBPSPs would be considered “processors.” Finally, NBPSPs that lend directly or issue monetary liabilities are active financial intermediaries whose activities should be captured in financial statistics. Figure 5 presents a type of hypothetical decision tree that would help with the classification of these entities

Technological challenges to incumbent banks

The increasing digitalization facilitated by technology, and its delivery through smart phones has led to potentially disruptive changes in finance and calls for rethinking of official statistics classifications. Many technology-centric firms have entered financial intermediation with the objective of competing with traditional banks. While there is no universally accepted nomenclature for these new entrants, they tend to be classified in two camps: challenger and neo banks. “Challenger banks,” are regulated banks that provide services through digital-only, branchless methods. “Neobanks” are non-regulated banks and innovate to find close substitutes to services provided by banks to compete for banking customers.

Challenger banks’ classification in official statistics is straightforward as they are subject to banking regulations and report their statistics in the same way that other banks do. Most challenger banks seek to attract large customer bases through their ease of use, low fees and competitive yields. Among the best known of these banks are Marcus, Discover Banks, and Varo Bank.

Neobanks are fintech firms that offer services that sometimes compete with banks and sometimes complement services provided by banks. They seek to be more user-friendly than incumbent banks, for example by working to assist depositors to avoid overdraft charges and by providing convenient apps. Because they often partner with banks, they may have their activity reported in the statistics of these banks, as is the case with Chime and Current, two U.S. examples that rely on partner banks to offer direct deposit and payment options utilizing debit cards. Revolut is an example in the U.K.

Neobanks in the embryonic stage may be classified as non-financial corporations but may require a change in classification as their activities mature. As a result, they require close attention by compilers of official statistics as they will likely be outside the scope of traditional financial data collections.

Mature neobanks may be difficult to classify depending on their funding sources and regulation. Those that are funded through securitization schemes and do not need to apply for a banking license will be difficult to classify in official statistics. Those that partner with incumbent banks or other service providers that and help them to expand their reach and meet know-your-customer (KYC) and anti-money laundering (AML) regulations, would likely be captured in the regular reporting of depository institutions’ statistics.

Classification Criteria for Digital Money in Financial and Monetary Statistics Manuals (FMSMs)

Since 2008, the introduction of decentralized, peer-to-peer electronic assets resembling cash has ushered in a number of potentially far-reaching changes to the financial system. Policy makers have called for the classification of these new digital instruments as crypto assets—rather than crypto currency (because of its high price volatility and lack of widespread use as media of exchange).

Discussions in the statistics community have reached a consensus to classify these digital assets as financial assets provided there exists a corresponding liability. However, no consensus has been reached thus far, including in the context of the SNA and BPM update, on the treatment of crypto assets without a corresponding liability (CAWLs), even if suitable to use or even designed to act as a general medium of exchange, or designed to act as medium of exchange within a platform only.

According to the 2008 SNA and BPM6, financial assets, by definition, must have a corresponding liability— with gold bullion held as official reserves as the only exception to this rule.¹⁷ This dictum is paramount to ensure cross-sectoral and cross-country consistency of the consolidated financial accounts.

Naturally this dictum works perfectly well for what economists refer to as inside money, which by definition has a corresponding liability. Inside money disappears if there is sufficient consolidation across the balance sheets in an economy as amounts due-to one party cancel out amounts due-from another, netting to zero (e.g., Garratt and Wallace (2018)). Examples of inside money include checking accounts in commercial banks as well as e-money, such as Alipay or M-pesa.

Currencies classified as outside money, such as hard currency and central bank reserves, do not disappear when all private balance sheets are consolidated. These financial assets are what we call in this paper, self-redeeming (Appendix V).

Currency and central bank reserves, while widely described as backed, cannot be redeemed into anything other than central bank money.¹⁸ We suggest that currency and central bank reserves, as they are not redeemable into the assets described as backing them, is best described as self-redeeming. Such a category would also be useful for the categorization of CAWLs. Hence, we propose to classify CAWLs (for example, Bitcoin) and monetary and non-monetary gold used for financial purposes as self-redeeming. Issuers of outside assets, such as fiat money or Bitcoin, do not commit to redeem it for any other asset. It is self-redeeming. It does not disappear when all private balance sheets are consolidated.¹⁹ In Appendix V, we emphasize the fact that the novelty of crypto assets is its technology, not its economic characteristics. The technologies of crypto assets (e.g., cryptography, distributed ledger, validation consensus mechanism, etc.) are deployed to address problems such as double spending or unbounded issuance (printing money), allowing crypto assets (potentially crypto currencies) to fulfill the classic roles of money as a medium of exchange. Other than this technology, there is little that is novel about crypto assets.²⁰,²¹ Figure 6 illustrates “the decision tree” emerging from the discussion in Appendix V.²²

In line with this reasoning, this paper—along with the ESCoE and ONS discussion paper (2021)— suggests creating a new “Valuables” category within the Financial Account and classifying self-redeeming assets, like CAWLs and financial gold within this new instrument (Appendix V).

A potential complication regarding our proposal may arise in cases where a CAWL, such as Bitcoin, is designated legal tender—as has been the recent case in a few countries. Would such designation affect the proposed classification of the CAWL as a self-redeemable financial asset? While, from a conceptual perspective, it could be argued that any asset held by the monetary authority as reserves for exchange rate management purposes (e.g., managing the exchange rate of Bitcoin vis-à-vis the US dollars) could be classified as international reserves, there are strict limits on what qualifies as international reserves. Because of those limits, it is not currently possible to classify crypto assets as international reserves. As a result, we propose that both public and private holdings of these assets should be classified in the new financial valuables category.

As the discussion about possible legal tender status indicates, the characteristic of self-redeemability does not exhaust the classification of all cyber assets—only some of which could potentially work as currencies. Further classifications may emerge, as future research sorts out several dimensions of crypto asset features. Specifically, whether a jurisdiction may deem a cryptocurrency to be legal tender; whether a cryptocurrency is programmable; whether the cryptocurrency is “native"—not tied to the use of a particular product or service, such as Bitcoin and Ethereum; whether the cryptocurrency builds on a native currency, such as decentralized applications (DApps) or non-fungible tokens (NFTs); or, like many initial coin offerings (ICOs) aimed at the funding products usually described, sometimes only loosely, in a “white paper.” Given the diverse and growing landscape of cryptocurrencies, this will be an important field of research. To give a flavor of the type of analysis that might take place in the future, we illustrate a potential taxonomy of a particular type of crypto asset—the fast-growing stablecoins.

Stablecoins

The emergence of crypto assets led to the rise of stablecoins, including custodial (tokens issued on a blockchain pledging to maintain a stable value with respect to an existing currency, commodity, or index) and algorithmic or undercollateralized coins. Specifically, algorithmic stablecoins automatically increase or decrease the supply of the coin depending on various conditions, such as a change in its price or in the backing assets that users provide to the system. Figure 7 depicts a bird’s eye view classification of stablecoins. The focus of policy discussions, as the focus in this paper, has been on stablecoins issued by institutions of either the custodial or central bank digital currency (CBDC) type.

Custodial stablecoins have grown quickly (although with a recent slowdown in growth rates) as shown in Figure 8. While in the aggregate (e.g., Thether, Circle’s USD, Paxos, etc.) they represent a small portion of the overall capital markets, their growth has attracted the attention of incumbent traditional intermediaries and policymakers.

Despite the strong interest in stablecoins, the technical risk they pose may not be fully understood. As explained by Narula (2021),²³ most centralized stablecoins operate as software running on decentralized blockchains. And the software and the underlying blockchain platforms they run on, are subject to technical and operational risk—a fact that may not be fully appreciated by their users and regulators.²⁴ The recent failure of Bean, a stablecoin associated with Beanstalk, an etherium protocol, is a cautionary tale for so-called algorithmic stablecoins. More recently, the failure of Terra, an algorithmic stablecoin provided yet another illustration of their instability.²⁵

Abstracting from these risks, classification of stablecoins in terms of their backing and liquidity is evolving making their classification for official statistics purposes difficult. In Lipton etal.’s (2020) words, “We ought to ask ourselves: are stablecoins … simply old wine in new bottles?”²⁶ The challenge is to identify when stablecoins are “old wine” (i.e., a new iteration of well-established institutions such as depository institution or money market funds) in digital form and when they are truly new type of assets (e.g., Bitcoin or Ethereum).

Key factors that determine the classification of stablecoins in official statistics include the type of assets they are backed with and the conditions for withdrawal or redemption of the stablecoins.²⁷ “To maintain a stable value relative to fiat currency, many stablecoins offer a promise or expectation that the coin can be redeemed at par upon request. These stablecoins are often advertised as being supported or backed by a variety of “reserve assets.” However, as indicated in PWG, page 4:

“Stablecoin redemption rights can also vary considerably, in terms of both who may present a stablecoin to an issuer for redemption and whether there are any limits on the quantity of coins that may be redeemed. Some issuers are permitted under the terms of the arrangement to postpone redemption payments for seven days, or even to suspend redemptions at any time, giving rise to considerable uncertainty about the timing of redemptions. As a further point of variation, stablecoins also differ in the nature of the claim provided to the user, with some providing a claim on the issuer and others providing no direct redemption rights to users”

Depending on these factors stablecoins may or may not resemble depository institutions. Stablecoins that offer rights to immediate redemption of the backing assets should probably be classified as depository institutions. Stablecoins that are not redeemable may more closely resemble ETFs, particularly if there are procedures in place to increase or decrease the amount of tokens through purchases or sales by the sponsor or authorized participants with the intention of maintaining a stable value.²⁸ However, because in most cases it may be unclear both what the backing assets are and the conditions under which tokens may be redeemed, stablecoins may not necessarily resemble either of these types of financial institutions.

Classification of custodial stablecoins in official statistics would depend on their reserves and the rights to redemption. If, for example, a stablecoin was associated with a central bank account, all proceeds were invested in central bank reserves, and tokens were immediately redeemable into a bank transfer, then the stablecoin issuer could be classified as a depository institution with a narrow asset base, i.e., a narrow bank. If the stablecoin held reserves at regulated deposit taking institutions that were immediately redeemable, it could be classified as a depository institution. If a stablecoin did not allow redemption of its tokens, it could be classified as a (potentially constant) NAV MMF or ETF. Figure 9 features an illustration of the type of decision tree that needs to be endorsed by official accounts methodologists for the classification of stablecoins.

Central Bank Digital Currency

While the taxonomy of CBDCs is still evolving, the BIS defines CBDCs as a form of digital money, denominated in the national unit of account, and representing a direct liability of the central bank (BIS 2021). So far, two variants are widely recognized: wholesale CBDCs (restricted to use by financial intermediaries for interbank transfers and wholesale transactions); and retail CBDCs (for day-to-day payments and transfers by the wider economy). Wholesale CBDC could be more flexible than more conventional settlement systems, for example by allowing settlement outside of current hours of operation or from participants outside the jurisdiction of the settlement system. Retail CBDCs, currently being tested and studied by central banks, would offer a widely available central bank liability in a more convenient form than paper currency. CBDCs can be account-based or token-based depending on the design choices that authorities make.²⁹ A key difference between CBDCs and stablecoins is that CBDCs are issued by a monetary authority, not the private sector. In general, narrow banks, which would offer accounts backed fully by central bank reserves, are not considered CBDCs by all analysts because the liability held by the retail customer is a liability of a private bank, and not the central bank.³⁰ Such narrow bank deposits are sometimes referred to as “synthetic CBDCs.”

Various authors have suggested that CBDCs can have benefits including improved financial inclusion, improved competition in the banking system, enabling of better tools for the implementation of monetary policy, and faster and more secure finality of payment settlement. One question is whether ongoing reforms to the current retail payments system will be enough to achieve faster and more secure finality of payment settlement—making the adoption of CBDC less urgent. Box 4 reports on two recent reforms to improve current retail payment systems drawing on commercial bank accounts—CODI in Mexico and PIX in Brazil.

^{Box 4.}

Central Bank Retail Payment Systems

An important touted benefit of CBDC has been a potential faster, cheaper and more secure retail payment settlement system. The question is whether ongoing reforms to the current system would suffice to achieve this goal, potentially making CBDC redundant. Below, we compare two such recent reforms, CODI and PIX.

In September 2019, Mexico introduced Codi, a payment system with many similarities to Brazil’s Pix. Both systems are available to virtually all transaction account holders for sending payments. A critical difference, however, is that Pix takes several key steps to promote universal access, including mandating that large financial institutions participate and that all participating PSPs provide their customers with all the functionalities needed for initiating and receiving instant payments in their mobile applications. Also, Codi data are stored at each financial institution, while Pix data are stored at the central bank. Compared to Pix, Codi was slower to introduce the capacity to make transfers to and from the government and participation in Codi is more restrictive; allowing participation only by financial institutions that are members of SPEI, the Bank of Mexico’s real-time gross settlement (RTGS) system.

Codi is less widely used in Mexico than Pix is in Brazil, with monthly transactions numbering in the hundreds of thousands and volume of transactions amounting to around 150 million Mexican pesos as of April 2021. Usage of Pix, on the other hand, has soared since its launch. By end-February 2022 (15 months after launch), 114 million individuals, or 67 percent of the Brazilian adult population, had either made or received a Pix transaction. Moreover, 9.1 million companies have signed up – fully 60 percent of firms with a relationship in the national financial system. The volume of transactions, initially around R$ 30 billion, was over R$ 600 billion by February 2022 (about twice the amount of currency in circulation). Over three quarters of transactions are between two individuals, but business to business transactions account for about a third of the total volume of transactions. Transactions with the government are possible, but the number and volume are negligible. Pix is used by people of all ages, with only a somewhat larger though gradually declining share of younger users. A 2021 article in Bloomberg reports anecdotes of Pix being used by very small businesses and even panhandlers. Source: BCB

Pix allows for near instantaneous (within a few seconds) transfers of cash at any time of day—including when offline—at zero cost for individuals (other than in a small set of circumstances) and low cost for businesses. Transactions can take place using only cellphones, though other avenues of transacting are possible, including at bank branches and ATMs. Security mechanisms are in place to detect fraud and to quickly reverse transactions when it is detected, and transactions occur over a protected network. There is no minimum transaction size limit, but participants may establish upper limits per payee, day, or month. Participants may join Pix with usernames that don’t reveal personal information, but that are linked to accounts that are not anonymous, albeit not revealed to every counterparty to a transaction.

Pix transactions are made to and from “transactional accounts", which include demand deposit, savings, or prepayment accounts operated by a bank or other financial institution. Pix can also be used to obtain cash, including at ATMs. Pix participants do not extend credit, and there are no balances associated with a Pix account; only with the accounts that Pix is linked to.

Pix has resulted in substantial reductions in costs for users. Pix costs an average of 0.22 percent of a transaction’s value for merchants, whereas debit cards in Brazil cost slightly above 1percent and credit cards reach 2.2 percent. Pix is also more competitive than credit cards, with fees of 1.7 percent in the United States, 1.5 percent in Canada and 0.3 percent in the European Union (BIS 2022).

Ongoing reforms to the current retail payments system, such as PIX, CODI, and the upcoming FedNow are aimed at addressing the speed and finality of payments. These reforms are ongoing and limited to specific countries. At the same time, a number of countries are actively studying the adoption of retail CBDCs. Thus, guidance for the classification of CBDC is needed.

Classifying CBDC in Official Statistics

A CBDC would presumably be classified as a liability of the central bank or monetary authority in the same manner as traditional currency (the likelihood that it is nonredeemable notwithstanding) making it straightforward to classify in official statistics.

Alternatively, were central banks to issue synthetic CBDCs, a two-tier system whereby commercial banks would be involved in the issuance of CBDC, banks would act as intermediaries issuing CBDC that would represent a claim on the central bank. In that case, it could also be that the synthetic CBDC could represent a claim on the issuing commercial banks, backed by deposits at the central bank. In this latter case, CBDC could be classified like a demand deposit, as “inside” money. Either classification would be a relatively straightforward application of current statistical methodology.

What Next?

The analysis so far illustrates data gaps arising from innovations in financial instruments that have been facilitated by technology and the need for additional methodological guidance to close these gaps—to assist in the early detection of financial vulnerabilities. Additional enhancements to both national accounts, financial sectors and instrument, and basic research in these areas should contribute to better monitoring of financial developments and inform economic policy making. To that end we list some recommendations.

IV. Main Recommendations

Update the international statistical standards’ classification systems. Provide cross-manual, common, and updated core definitions for each NBFI subsector, starting with the proposed taxonomy shown in Appendix III. The review of NBFI subsectors and selected financial instruments, assembled in Appendix I and illustrated in Appendix II, reveal both overlaps and inconsistencies in definitions across the STA manuals. A set of streamlined, common core definitions will aid statisticians in the assembly of necessary counterparty data and help ensure cross-domain compatibility. These core definitions should be supplemented with content appropriate to the domain.
Expand the FMSM classification systems to include NBFI financial institutions and activities currently not covered. As shown by the red text Appendix IV, there are several NBFI subsectors relevant to financial surveillance that are not discussed in the current suite of FMSMs nor in the proposed updated standards. Providing methodological guidance on these —their definitions, primary activities, and links to financial stability risks—will aid compilers in gaining support and traction for the collection of such data in jurisdictions where they are relevant, and support international comparability when data are published³¹
Incorporate additional discussion of NBFI’s connections to financial risks in SNA and MFS manuals. For example, NBFIs that allow immediate and at constant NAV withdrawals, such as by the authorized participants of exchange-traded funds, face higher funding risk—even when their liabilities are backed by short term assets, such as government fixed income securities of short maturity. Annex III outlines how this paper’s recommended NBFI breakdowns are associated with funding risk, excessive leverage, and credit risk. While there is a robust discussion of financial risks, particularly relating to depository corporations, in the Financial Soundness Indicators 2019 manual, compilers in other domains would also benefit from a better understanding of data needs for monitoring NBFI subsectors.
The international statistical community needs to take a more agile approach in the update of statistical manuals to ensure methodological guidance keeps pace with financial innovation. They will need to be able to quickly establish and adopt new international methodological guidance to keep pace with developments in NBFIs and Fintech.
Continue to inventory the coverage of relevant NBFI instruments, particularly those associated with sectorization, valuation, cross-border risks (not fully assessed in this paper) and those suggested through reader feedback. Identified instruments should be considered and documented in order to provide a more comprehensive view of potential risk exposures.
Statistical frameworks and Statistical Business Registers must seek to capture non-traditional providers that compete with traditional banks and report on them in a timely manner. The increasing digitalization of technology has not only broadened access to financial services but has allowed some of these non-traditional providers to operate without banking supervision and regulation, potentially leading to systemic risks to the financial system.
Decentralized, peer-to-peer crypto assets need to be monitored to the extent possible. They are rapidly evolving, are becoming entrenched in the financial system, and a lack of information will complicate the work of policymakers, investors, and other stakeholders.
Reporting guidance for crypto assets and stablecoins needs to be established. Self-redeeming private crypto assets are intended to act as financial assets and should be classified as such while recognizing their novel characteristics. The paper suggests the creation of a new Valuables category within the Financial Account to include self-redeeming assets, like Bitcoin and financial gold. The wide variety of rapidly growing stablecoins pose conceptual challenges to statisticians that should be addressed by the international statistical community under the aegis of Advisory Expert Group on National Accounts (AEG) and the IMF’s Balance of Payments Committee (BOPCOM).
The activity of fintech payment providers should be reported and monitored for purposes of both micro- and macro-financial stability. In cases where they store value within their organization, their assets should be reported and monitored on a national basis, consistent with the reporting required of banks.
Similarly, lending by fintechs should be reported and monitored in the same way as lending by banks. Fintech lending is an important category of loan origination in many markets, and, in some jurisdictions, the dominant sector for some loan types, such as for home mortgages in the U.S.
Funding structures for fintech lenders should be monitored for both leverage and funding risks. The risk of sudden decreases in funding availability is a key micro-prudential risk and may be associated with broader systemic risks.

V. Concluding Remarks and Next Steps

“Financial stability … is more about the tail of the probability distribution than the central probability” wrote Cunliffe (2017). And while tail events are infrequent, their consequences can be severe. We need granular enough data to recognize the buildup of potential vulnerabilities through time.

This paper seeks to update the guidance for the collection of NBFI and financial innovation data. Current official statistical guidance needs to be updated to reflect the rapid restructuring and innovation in the financial sector. The rapid growth of the NBFI and more broadly shadow banking and Fintech activities have many potential benefits, but also can create systemic risk—including from liquidity and deleveraging risks in the financial sector.

The paper reviews the current coverage of NBFI activities in official statistics (all statistical manuals), then proceeds with recommendations to enhance its clarity, consistency, and granularity. NBFI subsectors and instruments covered in Appendix III were selected to allow for meaningful assessments of potential systemic vulnerabilities with regards to financial risks. Not all the suggested NBFI granularity will apply to every economy, and national authorities must prioritize collection efforts around areas of greatest risk. Likewise, if any NBFI subsectors are particularly large, statistical compilers may want granularity even beyond that proposed in Appendix III to get a more nuanced view of a particular risks. It is worth emphasizing that sectorization, valuation, cross-border, and other relevant risk topics have been deferred for future versions of the paper.

The paper then focuses on selected Fintech developments and guidance. Drawing from economic principles and regulation, it illustrates how official statistics could provide guidance to classify selected Fintech activities. Going forward, because of difficulties in identifying what sets different Fintech products apart, the decentralized nature of many of them and the evolving and sometimes fragmented regulatory perimeter, classifying these emerging activities may not be so straightforward.

This paper contributes to ongoing work across the globe to address the need for timely, relevant statistics in an environment of rapid change. It compliments ongoing international consultations regarding the update of the SNA and BPM as well as work associated with the G-20 DGI. As these initiatives develop, their outcomes will likely influence aspects of this paper’s recommendations.³²

This paper focuses on the methodological guidance needed for compilation of statistics suitable for monitoring the build-up of risks in NBFIs and fintech activities. However, we acknowledge that competing statistical priorities, heterogeneity across countries, and even the changing nature of risks challenge this work. In addition to the risk topics such as sectorization, and valuation that are deferred for now, another area for future work is to elaborate on the financial stability risks posed by climate change and biodiversity loss. As this field is rapidly evolving, there is a strong appetite for statistical guidance on data and indicators that could inform policy makers on climate-related financial stability risks. Anticipated work coming out of the new DGI will likely help usher along these efforts.

Because a clear and up-to-date taxonomy is a key pillar for NBFI and Fintech data collection, Annex III needs to enjoy significant consensus—even as it is recognized that not all countries will need or be able to gather these data in the short run. Readers are also encouraged to share their views about the level of degree of granularity proposed in Annex III. Is this the appropriate level of granularity or are broader aggregates enough for financial stability analysis? Are their suggested enhancements to the common taxonomy recommendations?

Once there is some agreement on the NBFI subsectors, further deliberation must consider the necessary instrument detail needed to best monitor their financial risks. For example, ideally, all NBFIs should provide information on the degree of their investors’ leverage or the presence of leveraged investors to monitor the possibility of funding and market liquidity spirals on levered NBFI. What degree of balance sheet detail would be needed for these financial subsectors? While some amount of from-whom-to-whom detail is necessary for assessments of cross-sector exposures, what degree of instrument aggregation is appropriate? Developing Table examples, such as those in the FSI Guide, would be a useful tool to encourage the collection of the agreed data.

Appendix I. Current NBFI Taxonomy in Official Statistics

Appendix II. Guidance for Taxonomy Updates

Appendix III. Recommended Taxonomy of NBFI Subsectors, Including Potential Sources of Financial Stability Risk

Non-Bank Financial Intermediaries	Potential Source of Financial Stability Risks	Suggested Updates
•Money market funds		Money market funds (MMFs) are collective investment schemes that raise funds by issuing shares or units to the public. The proceeds are invested primarily in money market instruments, MMF shares or units, transferable debt instruments with a residual maturity of not more than one year, bank deposits and instruments that pursue a rate of return that approaches the interest rates of money market instruments. MMF shares can be transferred by cheque or other means of direct third-party payment.
constant NAV	Maturity mismatch, collateral management, cliff and run effects if NAV falls below, or is expected to fall below, critical level. Counterparty, sector, concentration risk (as in Reserve Primary Fund with a large Lehman position). Foreign exchange rate risk.	MMFS that aim to maintain a stable share price (typically one unit of currency). Also referred to as “stable NAV MMFS,” allow withdrawal at par value so long as NAV remains above critical level. The NAV is the value of a fund’s assets less the value of its liabilities, including operating expenses.
variable NAV	Maturity mismatch, collateral management, excessive management of NAV if coarsely measured. Counterparty, sector, concentration.	MMFS that do not aim to maintain a stable share price (share prices fluctuate). Also referred to as “floating NAV MMFS.” The definition must specify the digits to which the NAV is measured.
government	Maturity mismatch, collateral management. Counterparty risk in reverse repo positions.	Pooled investment funds that hold primarily government securities. These funds may invest in short-term securities, including Treasury securities, or reverse repos backed by Treasury securities or by Treasury-collateral.
prime	Maturity mismatch, collateral management Counterparty, sector	Prime securities refer to high quality low risk investments with low yields. The Fund uses the proceeds to invest in certificates of deposit (CD) and high-grade commercial paper (CP).
Non-money market investment funds		Collective investment schemes that raise funds by issuing shares or units to the public. The proceeds are invested predominantly in long-term financial assets and in nonfinancial assets (usually real estate). Non-MMF investment funds may also invest a small percentage of their total assets in highly liquid short-term financial instruments to ensure that requests to redeem shares or units are met without delay.
of which: open end	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Open Ended Non-MMFS can issue new shares and redeem existing shares on a continuous basis. Therefore, the price of open-end fund shares varies in direct proportion to the variation of the fund’s net asset value (NAV)-.
of which: closed end	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Closed end non-MMFs raise a fixed share of capital, during an initial subscription period. Thereafter, investors entering or leaving the fund must buy or sell existing shares (i.e., private equity/venture capital, segregated funds).
Equity funds	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector Foreign exchange rate risk	Non-MMFS that primarily invest in equity instruments.
Fixed income funds	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Non-MFFS that primarily invest in longer-term (original maturity more than one year) debt instruments such as government, municipal, and corporate bonds.
Index funds	Model, price, ineffective hedges Liquidity of markets in which indexed securities trade.	Index-tracking funds that mirror the performance of a specific group of shares.
Fund of funds	Model, price, ineffective hedges Liquidity of markets in which indexed securities trade.	Hold a portfolio of other investment funds shares rather than investing directly, in shares, bonds or other securities. There are different types of funds of funds, i.e., mutual, hedge, private equity or investment trust funds of funds.
•Hedge funds	Leverage, maturity mismatch, short positions, collateral management Leverage lending, credit, sector, OTC contracts Price, model risk, ineffective hedges. Sovereign risk from positions. Foreign exchange rate risk.	Any private investment fund (a) with investment advisers that may be paid a performance fee or allocation, calculated by taking into account unrealized gains; that may borrow an amount in excess of one-half of its net asset value (including any committed capital) or may have gross notional exposure in excess of twice its net asset value (including any committed capital); or (c) that may sell securities or other assets short or enter into similar transactions (other than for the purpose of hedging currency exposure or managing duration); (d) impose initial lock-up periods and general redemption notice periods, prior to specific redemption dates.
Private equity funds	Model, price, ineffective hedges Maturity mismatch if investors not subject to lock-up period Quality of investment	See closed end non-MMFs
Venture capital	Model, price, ineffective hedges	Venture capital fund refers to an investment fund that primarily invests the financial capital of third-party investors in early-stage corporations that are highly leveraged for the standard capital markets or banks.
Exchange-traded funds	Model, price, ineffective hedges Underlying market liquidity; credit or liquidity stresses in authorized participants.	Types of index-tracking funds that are traded intraday and are usually actively managed to accurately mirror the performance of an index. Because the structure of ETFs allows major market participants to redeem ETF shares for a “basket” of the fund’s underlying assets, the market price of an ETF trades in a narrow range very close to its NAV. Shares trade in secondary markets and are generally redeemed in-kind only by selected intermediaries.
Real estate investment trusts (REITs)	Counterparty and collateral value	Real Estate Investment Trusts (REITs) are of two types - equity REITs generate rental income through the owning and managing of real estate properties while mortgage REITs invest in financial assets backed by real estate, including mortgages and mortgage-backed securities. Both types are corporations that issue units, giving their investors access to the income generated by the trust’s portfolio.
Insurance Corporations	Model, price, ineffective hedges Counterparty, sector Leverage, maturity mismatch, short positions, collateral management	Insurance corporations consist of incorporated, mutual, and other entities whose principal function is to provide life, accident, sickness, fire, theft or other forms of insurance to individual institutional units or groups of units or reinsurance services to other insurance corporations. Insurance corporations may also provide investment funds to their policy holders and other retail investors.
Pension Funds		The pension funds subsector consists of autonomous pension funds that are established to provide retirement benefits for specific groups of individuals. These pension funds have their own separate sets of pension-fund assets and liabilities, with specific obligations to their contributors. Pension schemes can be established on a voluntary basis, or they can be compulsory with mandated contributions from the employee, employer, or both. Pension fund contributions by participants are invested in a mix of public-market and private-market assets
of which: Defined benefits	Leverage, maturity mismatch, short positions, collateral management Counterparty, sector, OTC contracts Model, price, ineffective hedges Sovereign risk from positions	Future retirement benefits are determined by an actuarial formula related to participants’ length of service and salaries, expected retirement ages, mortality rates, etc. Pension funds invests contributions in various securities to align returns with payouts.
of which: Defined contributions	Leverage, maturity mismatch, short positions, collateral management Counterparty, sector, OTC contracts Model, price, ineffective hedges Sovereign risk from positions	Benefits to be received by a participant are based on contributions to the pension fund and the investment performance of the fund, with no guarantee from the employer.
Sovereign Wealth Funds	Leverage, maturity mismatch, short positions, collateral management. Counterparty Model, price, ineffective hedges. Sovereign risk from positions.	Vehicles managed by state-affiliated entities, often focused on long-term illiquid assets.
Market Intermediaries
•Securities dealers	Leverage, maturity mismatch, short positions, collateral management. Counterparty, leverage lending, sector, OTC contracts. Model, price, ineffective hedges. Sovereign risk from positions.	Securities dealers use client relationships or own inventory to facilitate client trades. They often enable leverage for their clients.
•Principal trading firms	Leverage, maturity mismatch, short positions, collateral management. Counterparty, leverage lending, sector, OTC contracts. Model, price, ineffective hedges. Sovereign risk from positions.	High-frequency (trading using extraordinarily high speed and sophisticated programs for generating, routing, and executing orders) buyers and sellers in electronic markets, holding minimal end-of-day inventories.
•Central clearing counterparties	Settlement risk, model risk bearing on margin requirements. Counterparty, settlement risk.	They act as counterparties to holders of certain financial contracts, netting and managing counterparty risk. Provide clearing and settlement transactions in securities and derivatives, taking the financial risk of the transaction between counterparties on their own account, and becoming the counterparty of the original buyer and seller.
Financial Market Infrastructures
•Securities markets, exchanges and electronic platforms	Settlement risk Counterparty	Marketplaces for trading securities and/or financial contracts like derivatives.
Financial Instruments
Secured financing transactions	Settlement risk, including risk of failure to deliver security in a timely manner, Counterparty risk, model risk.	Transactions such as repurchase agreements, reverse repurchase agreements, security lending and borrowing, and margin lending transactions, where the value of the transactions depends on market valuations of the security sold and the transactions are often subject to margin agreements.
Derivatives	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Financial derivatives are financial instruments that are linked to a specific financial instrument, indicator, or commodity, through which specific financial risks (e.g., interest rate risk, foreign exchange risk, equity and commodity price risk, and credit risk) can be traded in their own right in financial markets. The value of a financial derivative depends on the price of the underlying item: the reference price. The reference price may relate to a commodity, a financial asset, an interest rate, an exchange rate, another derivative, or a spread between two prices. The derivative contract may also refer to an index or a basket of prices.
•Options	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Options are contracts that give the purchaser of the option the right, but not the obligation, to buy (a “call” option) or to sell (a “put” option) a particular financial instrument or commodity at a predetermined price (the “strike” price) within a given time span (American option) or on a given date (European option).
•Forwards and related instruments (i.e., futures)	Counterparty, leverage lending, sector, Model, price, ineffective hedges	A forward contract is an unconditional financial contract that represents an obligation for settlement on a specified date. Futures and other forward contracts are typically, but not always, settled by the payment of cash or the provision of some other financial instrument rather than the actual delivery of the underlying item and therefore are valued and traded separately from the underlying item.
•Swaps	Counterparty, leverage lending, sector, Model, price, ineffective hedges	A swap contract involves the counterparties exchanging, in accordance with prearranged terms, cash flows based on the reference prices of the underlying items. Swap contracts classified as forward-type contracts include currency swaps, interest rate swaps, cross-currency interest rate swaps, and equity swaps. Under a swap contract, the obligations of each party may arise at different times
of which: interest rate swap contract	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Fixed-rate payments swapped for floating-rate payments.
of which: foreign currency swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Payments in one currency swapped for payments in another currency.
of which: cross-currency interest-rate swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Fixed-rate payments in one currency swapped for floating-rate payments in another currency.
of which: equity swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	One party’s swapped payment is based on the performance of a stock price or stock index. The other party’s swapped payment can be based on a fixed or floating rate, another stock price, or a stock index.
of which: forward rate agreement (FRA)	Counterparty, leverage lending, sector, Model, price, ineffective hedges	An over-the-counter obligation that applies a predetermined interest rate to a notional principal amount over a specified future time period.
•Credit derivatives	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Credit derivatives are financial derivatives whose primary purpose is to trade credit risk

Non-Bank Financial Intermediaries	Potential Source of Financial Stability Risks	Suggested Updates
•Money market funds		Money market funds (MMFs) are collective investment schemes that raise funds by issuing shares or units to the public. The proceeds are invested primarily in money market instruments, MMF shares or units, transferable debt instruments with a residual maturity of not more than one year, bank deposits and instruments that pursue a rate of return that approaches the interest rates of money market instruments. MMF shares can be transferred by cheque or other means of direct third-party payment.
constant NAV	Maturity mismatch, collateral management, cliff and run effects if NAV falls below, or is expected to fall below, critical level. Counterparty, sector, concentration risk (as in Reserve Primary Fund with a large Lehman position). Foreign exchange rate risk.	MMFS that aim to maintain a stable share price (typically one unit of currency). Also referred to as “stable NAV MMFS,” allow withdrawal at par value so long as NAV remains above critical level. The NAV is the value of a fund’s assets less the value of its liabilities, including operating expenses.
variable NAV	Maturity mismatch, collateral management, excessive management of NAV if coarsely measured. Counterparty, sector, concentration.	MMFS that do not aim to maintain a stable share price (share prices fluctuate). Also referred to as “floating NAV MMFS.” The definition must specify the digits to which the NAV is measured.
government	Maturity mismatch, collateral management. Counterparty risk in reverse repo positions.	Pooled investment funds that hold primarily government securities. These funds may invest in short-term securities, including Treasury securities, or reverse repos backed by Treasury securities or by Treasury-collateral.
prime	Maturity mismatch, collateral management Counterparty, sector	Prime securities refer to high quality low risk investments with low yields. The Fund uses the proceeds to invest in certificates of deposit (CD) and high-grade commercial paper (CP).
Non-money market investment funds		Collective investment schemes that raise funds by issuing shares or units to the public. The proceeds are invested predominantly in long-term financial assets and in nonfinancial assets (usually real estate). Non-MMF investment funds may also invest a small percentage of their total assets in highly liquid short-term financial instruments to ensure that requests to redeem shares or units are met without delay.
of which: open end	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Open Ended Non-MMFS can issue new shares and redeem existing shares on a continuous basis. Therefore, the price of open-end fund shares varies in direct proportion to the variation of the fund’s net asset value (NAV)-.
of which: closed end	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Closed end non-MMFs raise a fixed share of capital, during an initial subscription period. Thereafter, investors entering or leaving the fund must buy or sell existing shares (i.e., private equity/venture capital, segregated funds).
Equity funds	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector Foreign exchange rate risk	Non-MMFS that primarily invest in equity instruments.
Fixed income funds	Model, price, ineffective hedges Maturity mismatch, collateral management, short positions Counterparty, sector	Non-MFFS that primarily invest in longer-term (original maturity more than one year) debt instruments such as government, municipal, and corporate bonds.
Index funds	Model, price, ineffective hedges Liquidity of markets in which indexed securities trade.	Index-tracking funds that mirror the performance of a specific group of shares.
Fund of funds	Model, price, ineffective hedges Liquidity of markets in which indexed securities trade.	Hold a portfolio of other investment funds shares rather than investing directly, in shares, bonds or other securities. There are different types of funds of funds, i.e., mutual, hedge, private equity or investment trust funds of funds.
•Hedge funds	Leverage, maturity mismatch, short positions, collateral management Leverage lending, credit, sector, OTC contracts Price, model risk, ineffective hedges. Sovereign risk from positions. Foreign exchange rate risk.	Any private investment fund (a) with investment advisers that may be paid a performance fee or allocation, calculated by taking into account unrealized gains; that may borrow an amount in excess of one-half of its net asset value (including any committed capital) or may have gross notional exposure in excess of twice its net asset value (including any committed capital); or (c) that may sell securities or other assets short or enter into similar transactions (other than for the purpose of hedging currency exposure or managing duration); (d) impose initial lock-up periods and general redemption notice periods, prior to specific redemption dates.
Private equity funds	Model, price, ineffective hedges Maturity mismatch if investors not subject to lock-up period Quality of investment	See closed end non-MMFs
Venture capital	Model, price, ineffective hedges	Venture capital fund refers to an investment fund that primarily invests the financial capital of third-party investors in early-stage corporations that are highly leveraged for the standard capital markets or banks.
Exchange-traded funds	Model, price, ineffective hedges Underlying market liquidity; credit or liquidity stresses in authorized participants.	Types of index-tracking funds that are traded intraday and are usually actively managed to accurately mirror the performance of an index. Because the structure of ETFs allows major market participants to redeem ETF shares for a “basket” of the fund’s underlying assets, the market price of an ETF trades in a narrow range very close to its NAV. Shares trade in secondary markets and are generally redeemed in-kind only by selected intermediaries.
Real estate investment trusts (REITs)	Counterparty and collateral value	Real Estate Investment Trusts (REITs) are of two types - equity REITs generate rental income through the owning and managing of real estate properties while mortgage REITs invest in financial assets backed by real estate, including mortgages and mortgage-backed securities. Both types are corporations that issue units, giving their investors access to the income generated by the trust’s portfolio.
Insurance Corporations	Model, price, ineffective hedges Counterparty, sector Leverage, maturity mismatch, short positions, collateral management	Insurance corporations consist of incorporated, mutual, and other entities whose principal function is to provide life, accident, sickness, fire, theft or other forms of insurance to individual institutional units or groups of units or reinsurance services to other insurance corporations. Insurance corporations may also provide investment funds to their policy holders and other retail investors.
Pension Funds		The pension funds subsector consists of autonomous pension funds that are established to provide retirement benefits for specific groups of individuals. These pension funds have their own separate sets of pension-fund assets and liabilities, with specific obligations to their contributors. Pension schemes can be established on a voluntary basis, or they can be compulsory with mandated contributions from the employee, employer, or both. Pension fund contributions by participants are invested in a mix of public-market and private-market assets
of which: Defined benefits	Leverage, maturity mismatch, short positions, collateral management Counterparty, sector, OTC contracts Model, price, ineffective hedges Sovereign risk from positions	Future retirement benefits are determined by an actuarial formula related to participants’ length of service and salaries, expected retirement ages, mortality rates, etc. Pension funds invests contributions in various securities to align returns with payouts.
of which: Defined contributions	Leverage, maturity mismatch, short positions, collateral management Counterparty, sector, OTC contracts Model, price, ineffective hedges Sovereign risk from positions	Benefits to be received by a participant are based on contributions to the pension fund and the investment performance of the fund, with no guarantee from the employer.
Sovereign Wealth Funds	Leverage, maturity mismatch, short positions, collateral management. Counterparty Model, price, ineffective hedges. Sovereign risk from positions.	Vehicles managed by state-affiliated entities, often focused on long-term illiquid assets.
Market Intermediaries
•Securities dealers	Leverage, maturity mismatch, short positions, collateral management. Counterparty, leverage lending, sector, OTC contracts. Model, price, ineffective hedges. Sovereign risk from positions.	Securities dealers use client relationships or own inventory to facilitate client trades. They often enable leverage for their clients.
•Principal trading firms	Leverage, maturity mismatch, short positions, collateral management. Counterparty, leverage lending, sector, OTC contracts. Model, price, ineffective hedges. Sovereign risk from positions.	High-frequency (trading using extraordinarily high speed and sophisticated programs for generating, routing, and executing orders) buyers and sellers in electronic markets, holding minimal end-of-day inventories.
•Central clearing counterparties	Settlement risk, model risk bearing on margin requirements. Counterparty, settlement risk.	They act as counterparties to holders of certain financial contracts, netting and managing counterparty risk. Provide clearing and settlement transactions in securities and derivatives, taking the financial risk of the transaction between counterparties on their own account, and becoming the counterparty of the original buyer and seller.
Financial Market Infrastructures
•Securities markets, exchanges and electronic platforms	Settlement risk Counterparty	Marketplaces for trading securities and/or financial contracts like derivatives.
Financial Instruments
Secured financing transactions	Settlement risk, including risk of failure to deliver security in a timely manner, Counterparty risk, model risk.	Transactions such as repurchase agreements, reverse repurchase agreements, security lending and borrowing, and margin lending transactions, where the value of the transactions depends on market valuations of the security sold and the transactions are often subject to margin agreements.
Derivatives	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Financial derivatives are financial instruments that are linked to a specific financial instrument, indicator, or commodity, through which specific financial risks (e.g., interest rate risk, foreign exchange risk, equity and commodity price risk, and credit risk) can be traded in their own right in financial markets. The value of a financial derivative depends on the price of the underlying item: the reference price. The reference price may relate to a commodity, a financial asset, an interest rate, an exchange rate, another derivative, or a spread between two prices. The derivative contract may also refer to an index or a basket of prices.
•Options	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Options are contracts that give the purchaser of the option the right, but not the obligation, to buy (a “call” option) or to sell (a “put” option) a particular financial instrument or commodity at a predetermined price (the “strike” price) within a given time span (American option) or on a given date (European option).
•Forwards and related instruments (i.e., futures)	Counterparty, leverage lending, sector, Model, price, ineffective hedges	A forward contract is an unconditional financial contract that represents an obligation for settlement on a specified date. Futures and other forward contracts are typically, but not always, settled by the payment of cash or the provision of some other financial instrument rather than the actual delivery of the underlying item and therefore are valued and traded separately from the underlying item.
•Swaps	Counterparty, leverage lending, sector, Model, price, ineffective hedges	A swap contract involves the counterparties exchanging, in accordance with prearranged terms, cash flows based on the reference prices of the underlying items. Swap contracts classified as forward-type contracts include currency swaps, interest rate swaps, cross-currency interest rate swaps, and equity swaps. Under a swap contract, the obligations of each party may arise at different times
of which: interest rate swap contract	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Fixed-rate payments swapped for floating-rate payments.
of which: foreign currency swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Payments in one currency swapped for payments in another currency.
of which: cross-currency interest-rate swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Fixed-rate payments in one currency swapped for floating-rate payments in another currency.
of which: equity swap	Counterparty, leverage lending, sector, Model, price, ineffective hedges	One party’s swapped payment is based on the performance of a stock price or stock index. The other party’s swapped payment can be based on a fixed or floating rate, another stock price, or a stock index.
of which: forward rate agreement (FRA)	Counterparty, leverage lending, sector, Model, price, ineffective hedges	An over-the-counter obligation that applies a predetermined interest rate to a notional principal amount over a specified future time period.
•Credit derivatives	Counterparty, leverage lending, sector, Model, price, ineffective hedges	Credit derivatives are financial derivatives whose primary purpose is to trade credit risk

Appendix IV. Proposed NBFI Breakdown

Financial subsectors and instruments listed in black correspond to breakdowns proposed via international consultation ahead of the update of the SNA and BP manual updates.¹ Breakdowns in red correspond to this paper’s suggested additional breakdowns for a forward-looking picture of financial sector vulnerabilities. Highlighted rows correspond to those NBFI subsectors and financial instruments in Appendix III.

Appendix V. On the Categorization of Crypto Assets in Official Statistics

Marco Espinosa-Vega and James McAndrews

In a recent paper, Rod Garratt and Neil Wallace review the distinction between two types of money, outside and inside. “Most economists distinguish between inside and outside money. Inside money is inside the economy in the sense that each unit is someone’s asset and someone else’s liability. That is, inside money disappears if there is sufficient consolidation across the balance sheets of agents in the economy. Outside money, in contrast, does not disappear when balance sheets are consolidated.”¹

Economists classify Federal Reserve notes and reserves issued by the central bank as outside money. This is justified by the following logic, from Ricardo Lagos, focusing on whether the assets are in zero net supply in the private sector: “Outside money is money that is either of a fiat nature (unbacked) or backed by some asset that is not in zero net supply within the private sector of the economy…Inside money is an asset representing, or backed by, any form of private credit that circulates as a medium of exchange. Since it is one private agent’s liability and at the same time some other agent’s asset, inside money is in zero net supply within the private sector.” ²

In official statistics, however, this distinction, between private and public money, and between inside and outside money, is overlooked. Instead, for accounting purposes, both notes and reserves of central banks are considered “liabilities backed” by the assets held by the central bank.

This has resulted in difficulties of categorizing some self-redeeming digital assets. Individuals are willing to use well-established monies or commodities as a medium of exchange because they believe “that others will do the same” in the future. And as explained in Garratt and Wallace, these self-redeeming digital currencies “do not yield utility as might ownership of a Picasso and is not an input into the production of other things as is farmland, a factory” (we note that such assets are not “valuables” in SNA terminology).

Self-Redeeming assets

The definition of assets with corresponding liabilities, in official statistics, overlooks a key difference between inside money and outside money. The paper by Kumhof et al. discusses this interpretation of central bank, or outside, money.³ It persuasively argues that central bank reserves and currency are not “liabilities” of the central bank in the conventional sense.

Consider a household that owns a deposit account at a commercial bank with positive balances—a financial asset. The household approaches the issuing bank and requests redemption of the bank’s deposit liability. The bank can do so in several ways: by providing the household with currency or with a deposit in a different bank.

Crucially, the bank has assets to “back” its deposit liabilities. Now consider a bank approaching the central bank to redeem currency. It is returned currency or reserves—another liability of the central bank. It is not offered some asset owned by the central bank, which is unlike the case with the commercial bank.

Therefore, we suggest a characterization of financial assets that is more aligned with the economic understanding of money: to classify financial assets as either redeemable into some other financial instrument, or, alternatively, as self-redeeming. Self-redeeming financial assets include financial gold (gold used for financial purposes), currency, and central bank reserves. Commercial bank deposits and other “conventional” near-monies, such as money market mutual fund shares, are redeemable into some other financial instrument.⁴

In this classification system, some crypto assets, such as bitcoin, would be catalogued as self-redeeming. Others, such as the liabilities of “stablecoins” that are redeemable into other financial instruments would not be classified as self-redeeming. It is important to note that central bank issued currency and reserves are “backed” by assets owned by the central bank, as well as being supported by legal tender status in law and other types of institutional support. In contrast, Bitcoin, for example, has no “backing.”

This approach would align the treatment of crypto assets with that of financial gold, as a financial asset without corresponding liability (in official statistics terminology), but it would also expand the general category to include currency and central bank reserves.

In this note we offer a new perspective, on the topic, which aligns with some recommendations made by Heys et .al. It likened crypto assets without corresponding liability to financial gold. Although gold prices are volatile, gold is used as a store of value—a quintessentially monetary objective, or property of “good money".

In line with this perspective, this paper recommends creating a new Valuables category within the Financial Account and classifying self-redeeming crypto assets, like Bitcoin, and financial gold within this new Valuables account. A more radical, albeit harder to implement, proposal, would have all outside money be classified into this new account.

In the following decision tree, all financial assets are classified as either a medium of exchange or not. Along the medium of exchange branch, we then classify such media as either self-redeemable or not. For monetary assets such as currency or demand deposits in commercial banks, this categorization is congruent with the economic notion of outside and inside money, respectively. The categorization as self-redeemable or not is more extensive, however, as it can be applied to crypto assets. An asset such as bitcoin, which acts as a medium of exchange does not have a corresponding liability, or, in other words, is self-redeemable. Stablecoins tokens that offer redemptions, in contrast, are more akin to commercial bank deposits. Redeemability is a clearer category than some have suggested, as we discussed in the notes above. General Accounts include fixed-income securities.

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Deposit taking institutions include mainly central banks and banks, though other deposit-taking institutions, such as credit unions or SACCOs are also included.

Non-Bank Financial Intermediation - Financial Stability Board (fsb.org)

While the additional conceptual guidance and taxonomy proposed in this paper would aid the compilation of macro statistics that can be used for macro-financial surveillance , such data will also need to be complemented with additional, higher frequency, and more granular and timely data for financial stability analysis.

Money market funds are classified as ODCs in Monetary and Financial Statistics (MFS), but for the purposes of this paper, money market funds are excluded from deposit taking institutions and included in the NBFI sector. FMSM include the 2008 System of National Accounts, Balance of Payments Manual 6, Monetary and Financial Statistics Manual and Compilation Guide, Government Finance Statistics Manual. The 2019 Financial Soundness Indicators Compilation Guide and the 2015 Handbook on Securities Statistics are also included in the overview of current NBFI taxonomy.

REITs can be legally incorporated entities or more generally, structured as trusts, where the units trade on stock exchanges. In the 2008 SNA, real estate activities by trusts or companies are classified under the non-financial corporate sector. Real estate funds are classified under the non-money market investment fund sector. More detail is provided in the 2015 Financial Production, Flows and Stocks SNA handbook, where a distinction is made between real estate funds, equity and mortgage REITs; however, equity and mortgage REITs are also described as types of real estate funds. Equity REITs, are economic entities that own, manage and operate commercial and retail properties and generate a surplus through real estate services. Mortgage REITs, on the other hand, are a type of real estate fund that typically invests in mortgages or mortgage-related assets like mortgage back securities (MBS). Real estate funds invest in debt and equity securities of companies/trusts that purchase real estate – equity REITS. Both real estate funds and Mortgage REITs are categorized separately within the non-MMF investment fund subsector, while equity REITs are to be classified as nonfinancial corporations. (Box 4.7) The guidance from the MFSMCG is like that of the Financial Production, Flows and Stocks handbook, where mortgage REITS are distinct from real estate funds. The only other manual that mentions REITs is the BPM6, which simply states that REITs are to be included in the investment fund subsector but makes no mention of the distinction between equity REITs and mortgage REITs nor other real estate funds (4.74).

Appendix I lists the current taxonomy of all the main NBFI categories—institutional investors and asset managers; market intermediaries; and financial market infrastructures—in official statistics, the Financial Soundness Indicators and the Handbook on Securities Statistics. These categories include relevant financial subsectors and financial instruments that will be detailed in the 2025 SNA and BPM update.

A common core taxonomy would use identical language across the manuals to minimize different interpretations of the same concept. However, beyond this common core, each manual may request additional granularity and provide examples and context appropriate to its domain.

Taxonomy from the 2008 SNA manual and the 2015 Financial Production, Flows and Stocks SNA handbook are grouped together in the SNA column. Seven additional NBFI subsectors proposed in Appendix III are currently not covered in any of the manuals and have therefore been excluded from this review. These subsectors are constant net asset value (NAV) MMFs, variable NAV MMFs, government MMFs, prime MMFs, equity funds, fixed income funds, and principal trading firms.

As documented, for the U.S., by Herman et al. (2015), nonbank credit tends to be more procyclical than bank credit. As explained in, for example, Cunliffe (2020), highly leveraged NBFI’s are exposed to “small price moves (leading) to large losses relative to the fund’s Net Asset Value, (with) … growing margin requirements (reducing) the amount of funding to support trading. This combination of losses and liquidity demands can very quickly generate pressure on funds to de-lever, with corresponding reductions in market liquidity. As demonstrated in, for example, (Shin et al. 2011), an important source of funding risk in the banking sector is the reliance on non-core funding (e.g. wholesale deposits) because movements away from core funding hides sudden funding reversal risks. And due to the complexity and opacity in some of NBFI funding linkages, funding reversal risks are more prominent in the NBFI sector. Kayshap (2021), illustrates with a hypothetical example, funding reversal risks arising from funding interconnections among pension funds, money market funds broker-dealers and hedge funds. As he explains, these funding chains “can create contagion in liquidity demands” in times of stress times. Funding chains across these NBFI create a cumulative need for liquidity that “can far exceed the liquidity needs of any one party in the chain". Funds are recycled in the NBFI system in that for “each link in the chain, one party’s perceived liquidity asset is another part’s runnable liability.” A real-life example of this hypothetical dash-for-cash was observed in March 2020, where non-government money market funds experienced significant redemptions.

See ETFGI reports the global ETFs industry gathered US $105.88 billion of net inflows in February 2022

For further discussion of the potential systemic risks posed by ETFs, see Bhattacharya and O’Hara (2020).

Additional NBFI subsectors and financial instruments that could be considered for coverage later include warehoused asset loans, insured versus uninsured deposits by investor, securitization funds, and interval investment funds.

Financial Stability Board, FinTech and market structure in financial services (February 2019)

In this section, we discuss fintechs that do not focus on crypto assets, but instead provide more conventional payment related services.

In Ehrentaud et al. (2021)’s words: “The NBPSP category comprises two nonoverlapping subcategories. They are (1) those offering storage of value in a payment account or on a device (for example, nonbank e-money institutions and post office Giro institutions) and (2) those not offering it themselves but relying on storage of value by others (e.g., merchant and ATM acquirers, payment initiation service providers and account information service providers).”

Gold bullion held as part of monetary gold is the only current exception to this rule, in that it is a financial asset that no counter party sector recognizes as a corresponding liability (2008 SNA §17.244).

Kumhof, Michael et al. Central Bank Money: Liability, Asset, or Equity of the Nation? (November 14, 2020). Cornell Legal Studies Research Paper 20-46, Available at SSRN: https://ssrn.com/abstract=3730608 or http://dx.doi.org/10.2139/ssrn.3730608.

Nor upon “failure” or “liquidation” of any institution involved is there a distribution of assets backing the money. This is in contrast to share-based, or equity, instruments, which have a claim on the assets of the enterprise upon liquidation. For a self-redeeming asset, no such claim either exists or would be honored.

In practice, however, it can be argued that Bitcoin and other CAWLs are not yet acting as a widely accepted medium of exchange.

Some crypto assets, such as bitcoin, also have a novel economic characteristic in that they are not sponsored by an individual or corporation, nor are they mutually owned. Instead, they are managed as collectives with voting rules, or with coalitions of participants determining policies. While these are novel organization types, they don’t strike us as related to the question of the statistical reporting of the volumes of these instruments.

For an alternative taxonomy contrasting traditional and digital money, see Adrian and Mancini-Griffoli (2021).

Neha Narula, “The Technology Underlying Stablecoins,” 23 September, 2021, https://nehanarula.org/2021/09/23/stablecoins.html.

Stablecoins run as smart contracts on blockchains. In many cases what we call one stablecoin (for example, Tether or USDC) runs on multiple different blockchains, as entirely separate tokens sharing a single backing. Users can choose which token on which blockchain to obtain, but some might be easier to obtain or use than others, as the tokens are accepted in smart contracts or as payment individually. Each blockchain is used as an accounting of who currently controls a stablecoin token. For example, USDC runs on five different chains: Ethereum (27.5B USDC), Algorand (345M USDC), Solana (2.9B USDC), Stellar (161M USDC), and Tron (206M USDC), with the intention of adding ten more. There is no “universal” USDC token—each USDC token is specific to one of these chains. This is often a point of confusion; for example, CoinMarketcap mistakenly says that “All of the USDCs in circulation are actually ERC-20 tokens, which can be found on the Ethereum blockchain.” This could be because Coinbase, one of the largest US cryptocurrency exchanges, currently only sells USDC issued on the Ethereum blockchain. However, Centre’s own website says that “USD Coin is an ERC-20 stablecoin,” which is not true of USDC issued on other chains.

See Sam Kessler, “Attacker drains $182 million from Beanstalk Stablecoin Protocol,” Coindesk, April 17, 2022. https://www.coindesk.com/tech/2022/04/17/attacker-drains-182m-from-beanstalk-stablecoin-protocol/.

Alexander Lipton, Aetienne Sardon, Professor Dr. Fabian Schär, and Christian Schüpbach, “Stablecoins, Digital Currency, and the Future of Money, 2020, available at https://arxiv.org/pdf/2005.12949.pdf.

President’s Working Group (PWG) on Financial Markets, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency, “Report on Stablecoins,” November 2021. https://home.treasury.gov/system/files/136/StableCoinReport_Nov1_508.pdf.

See Paul Kupiec, “Should stablecoins be regulated like banks, exchange-traded funds, or both?” December 7, 2021, The Hill, https://thehill.com/opinion/finance/584499-should-stablecoins-be-regulated-like-banks-exchange-traded-funds-or-both?rl=1.

See Morten Bech and Rod Garratt, “Central Bank Cryptocurrencies,” BIS Quarterly Review, September 2017, and Markets Committee, Committee on Payment and Settlement Systems, BIS, “Central Bank Digital Currencies,” March 2018, for descriptions and discussion of these types of CBDCs.

See Adrian, T., Mancini-Griffoli, T. (2019). The Rise of Digital Money, FINTECH NOTES, 19/01, 2019, p. 1–15 for a discussion of this option, called synthetic CBDCs.

An example of a link to a financial stability risk that should be included in the core definition is the frequency and parity at which the NBFI allows investor withdrawals.

For example, financial vehicle corporations engaged in securitization transactions will likely be considered in future versions of the paper for their role in dispersing credit risk.

See FITT Guidance Notes F.1 “More Disaggregated Institutional Sector and Financial Instrument Breakdowns” for further details.

Although equity REITs are part of the nonfinancial corporate sector and therefore not included in the non-MMF investment fund aggregates, a wholistic view of the potential inter-connectedness between real estate activities and financial intermediaries is important for policy makers to consider. Thus we include equity REITs in this context.

Rodney Garratt & Neil Wallace, 2018. “Bitcoin 1, Bitcoin 2, … An Experiment In Privately Issued Outside Monies,” Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1887–1897, July.

Ricardo Lagos, “2006 Inside and Outside Money” Federal Reserve Bank of Minneapolis Research Department Staff Report 374 May 2006.

Kumhof, Michael and Allen, Jason G and Bateman, Will and Lastra, Rosa M. and Gleeson, Simon and Omarova, Saule T., Central Bank Money: Liability, Asset, or Equity of the Nation? (November 14, 2020). Cornell Legal Studies Research Paper 20–46, Available at SSRN: https://ssrn.com/abstract=3730608 or http://dx.doi.org/10.2139/ssrn.3730608.

Equity is not a self-redeeming asset as in bankruptcy, the equity may be “redeemed” by allocation of the assets to be liquidated or conserved, ownership in a new enterprise, or proceeds from the sales of assets. A self-redeeming asset is one for which bankruptcy does not allow any transfer into a different type of claim. Consequently, EFT shares and MMMF shares, even though holders may not redeem them and “withdraw” assets from the fund, are not self-redeeming.

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Financial Innovation and Statistical Methodological Guidance—Key Considerations

Author:

Mr. Joe Crowley

Marco A Espinosa-Vega

Elizabeth Holmquist

Ken Lamar

Emmanuel Manolikakis

James McAndrews

, and

Holt Williamson

Volume/Issue:: Volume 2022: Issue 212

Publisher:: International Monetary Fund

ISBN:: 9798400224157

ISSN:: 1018-5941

Pages:: 63

DOI:: https://doi.org/10.5089/9798400224157.001

Keywords
I. Introduction
II. Non-Bank Financial Intermediation in Macro Statistics
III. Fintech in Financial and Macroeconomic Statistics Manuals
IV. Main Recommendations
V. Concluding Remarks and Next Steps
Appendix I. Current NBFI Taxonomy in Official Statistics
Appendix II. Guidance for Taxonomy Updates
Appendix III. Recommended Taxonomy of NBFI Subsectors, Including Potential Sources of Financial Stability Risk
Appendix IV. Proposed NBFI Breakdown
Appendix V. On the Categorization of Crypto Assets in Official Statistics
References

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^{Figure 1.}

Guidance for Taxonomy Updates^*

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^{Figure 2.}

The Growth and Global Reach of ETFs

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^{Figure 3.}

U.S.: Core and Non-Core Funding of the Financial Sector

(US Trillion)

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^{Figure 4.}

Runnable Liabilities

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^{Figure 5.}

Non-Bank Payment Service Providers (NBPSPs) Proposed Tree

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^{Figure 6.}

Hypothetical Financial Asset Decision Tree

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^{Figure 7.}

Types of Stablecoins

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^{Figure 8.}

Top Stablecoins by Market Capitalization

(in billions)

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^{Figure 9.}

Hypothetical Classification of Stablecoins

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^{Figure 1.}

Financial Asset Decision Tree

Non-Bank Financial Subsectors
Money Market Funds (MMFs) (S123)
Constant Net Asset Value MMFs
Variable Net Asset Value MMFs
Of which: Government
Of which: Prime
Non-Money Market Funds (non-MMFs) (S124)
Open end funds (S124a)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other open-end funds
Index funds
Fund of funds
Private equity funds
Of which: Venture capital
Exchange-traded funds
Closed end funds (S124b)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other closed-end funds
Funds of funds
Private equity funds
Of which: Venture capital
Other Financial Intermediaries (OFIs) (S125)
Financial vehicle corporations engaged in securitization transactions
Financial corporations engaged in lending (FCLs)
Security and derivative dealers
Specialized financial corporations
Of which: Principal trading firms
Other OFIs
Of which: Central clearing counterparties

Financial auxiliaries (S126)
Of which: Securities markets, exchanges and electronic platforms
Captive financial institutions and money lenders (S127)
Trusts, estate, and agency accounts
Corporate groups’ captive financial entities
Of which: Foreign owned SPE-type captives
Other captive finance companies and money lenders
Of which: Sovereign wealth funds
Insurance corporations (S128)
Non-life insurance corporations
Life insurance corporations
Pension funds (S129)
Defined benefit pension funds
Defined contribution pension funds

Selected Financial Instruments
Loans (F.4)
Of which: Repurchase agreements, securities lending with cash collateral, and margin lending
Derivatives and employee stock options (F.7)
(By market risk category)
Foreign exchange
Single-currency interest rate
Equity
Commodity
Credit
Other
(By instrument)
Options
Forwards and related instruments
Futures
Swaps
Of which: interest rate swap contracts
Of which: foreign currency swaps
Of which: cross-currency interest-rate swaps
Of which: equity swaps
Of which: forward rate agreement (FRA)
Credit derivatives
Marketable employee stock options
Other
(By trading venue and cleaning status)
Exchange traded
Over-the-counter (cleared)
Over-the-counter (not cleared)

Non-Bank Financial Subsectors
Money Market Funds (MMFs) (S123)
Constant Net Asset Value MMFs
Variable Net Asset Value MMFs
Of which: Government
Of which: Prime
Non-Money Market Funds (non-MMFs) (S124)
Open end funds (S124a)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other open-end funds
Index funds
Fund of funds
Private equity funds
Of which: Venture capital
Exchange-traded funds
Closed end funds (S124b)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other closed-end funds
Funds of funds
Private equity funds
Of which: Venture capital
Other Financial Intermediaries (OFIs) (S125)
Financial vehicle corporations engaged in securitization transactions
Financial corporations engaged in lending (FCLs)
Security and derivative dealers
Specialized financial corporations
Of which: Principal trading firms
Other OFIs
Of which: Central clearing counterparties

Financial auxiliaries (S126)
Of which: Securities markets, exchanges and electronic platforms
Captive financial institutions and money lenders (S127)
Trusts, estate, and agency accounts
Corporate groups’ captive financial entities
Of which: Foreign owned SPE-type captives
Other captive finance companies and money lenders
Of which: Sovereign wealth funds
Insurance corporations (S128)
Non-life insurance corporations
Life insurance corporations
Pension funds (S129)
Defined benefit pension funds
Defined contribution pension funds

Selected Financial Instruments
Loans (F.4)
Of which: Repurchase agreements, securities lending with cash collateral, and margin lending
Derivatives and employee stock options (F.7)
(By market risk category)
Foreign exchange
Single-currency interest rate
Equity
Commodity
Credit
Other
(By instrument)
Options
Forwards and related instruments
Futures
Swaps
Of which: interest rate swap contracts
Of which: foreign currency swaps
Of which: cross-currency interest-rate swaps
Of which: equity swaps
Of which: forward rate agreement (FRA)
Credit derivatives
Marketable employee stock options
Other
(By trading venue and cleaning status)
Exchange traded
Over-the-counter (cleared)
Over-the-counter (not cleared)

Non-Bank Financial Subsectors
Money Market Funds (MMFs) (S123)
Constant Net Asset Value MMFs
Variable Net Asset Value MMFs
Of which: Government
Of which: Prime
Non-Money Market Funds (non-MMFs) (S124)
Open end funds (S124a)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other open-end funds
Index funds
Fund of funds
Private equity funds
Of which: Venture capital
Exchange-traded funds
Closed end funds (S124b)
Real estate funds
Of which: Mortgage REITs
Equity REITs²
Equity funds
Bond (fixed income) funds
Mixed or balanced funds
Hedge funds
Other closed-end funds
Funds of funds
Private equity funds
Of which: Venture capital
Other Financial Intermediaries (OFIs) (S125)
Financial vehicle corporations engaged in securitization transactions
Financial corporations engaged in lending (FCLs)
Security and derivative dealers
Specialized financial corporations
Of which: Principal trading firms
Other OFIs
Of which: Central clearing counterparties

Financial auxiliaries (S126)
Of which: Securities markets, exchanges and electronic platforms
Captive financial institutions and money lenders (S127)
Trusts, estate, and agency accounts
Corporate groups’ captive financial entities
Of which: Foreign owned SPE-type captives
Other captive finance companies and money lenders
Of which: Sovereign wealth funds
Insurance corporations (S128)
Non-life insurance corporations
Life insurance corporations
Pension funds (S129)
Defined benefit pension funds
Defined contribution pension funds

Selected Financial Instruments
Loans (F.4)
Of which: Repurchase agreements, securities lending with cash collateral, and margin lending
Derivatives and employee stock options (F.7)
(By market risk category)
Foreign exchange
Single-currency interest rate
Equity
Commodity
Credit
Other
(By instrument)
Options
Forwards and related instruments
Futures
Swaps
Of which: interest rate swap contracts
Of which: foreign currency swaps
Of which: cross-currency interest-rate swaps
Of which: equity swaps
Of which: forward rate agreement (FRA)
Credit derivatives
Marketable employee stock options
Other
(By trading venue and cleaning status)
Exchange traded
Over-the-counter (cleared)
Over-the-counter (not cleared)

Topics

Countries

Series

About

Resources

Abstract

I. Introduction

II. Non-Bank Financial Intermediation in Macro Statistics

The State of Play

The Need for Additional Granularity

Recommendations for Taxonomy Updates and Additional Granularity

III. Fintech in Financial and Macroeconomic Statistics Manuals

Non-bank payment service providers

Technological challenges to incumbent banks

Classification Criteria for Digital Money in Financial and Monetary Statistics Manuals (FMSMs)

Stablecoins

Central Bank Digital Currency

Classifying CBDC in Official Statistics

What Next?

IV. Main Recommendations

V. Concluding Remarks and Next Steps

Appendix I. Current NBFI Taxonomy in Official Statistics

Appendix II. Guidance for Taxonomy Updates

Appendix III. Recommended Taxonomy of NBFI Subsectors, Including Potential Sources of Financial Stability Risk

Appendix IV. Proposed NBFI Breakdown

Appendix V. On the Categorization of Crypto Assets in Official Statistics

References

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