3. The Economics of Shadow Banking

Manmohan Singh
Published Date:
October 2016
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The couple of decades leading up to the Lehman crisis in 2008 witnessed rapid growth in financial intermediation whereby non-banks interact with banks. Coined under the rubric of “shadow banking”, the bank–non-bank nexus is largely seen as a form of regulatory arbitrage. However, this is an incomplete view, since there is genuine economic demand for such services. This chapter1 attempts to explain the economics that supports the demand/supply for this market, the systemic risks that can arise and the regulatory and broader policy implications.

To formulate a policy response to shadow banking, we need to understand the nuts and bolts of how these markets work. Collateral is used in a wide range of financial transactions: secured funding (mostly by non-bank investors), repurchase agreements (or repo), and hedging (primarily with over-the-counter (OTC) derivatives). As collateral is increasingly scarce, a key shadow-banking function is to mobilise and reuse collateral to support a large volume of transactions. So there is use of “capital” in shadow banking in the form of margins and overcollateralisation (via haircuts) in each transaction, much of which does not make it to the balance sheet. However, this capital remains “shadowy” and not easy to quantify relative to Basel’s 8% capital on the balance sheet for banks.

A globally integrated financial system needs to manage heightened counterparty risks. As aggregate economic activity rebounds and as traditional bank regulations become tightened, shadow banking will gain traction. The analysis allows us to outline four components of a comprehensive policy response:

  • ❑ addressing systemic risks within the shadow-banking system;

  • ❑ addressing demand-side pressures and how to accommodate a shortage of safe and liquid assets;

  • ❑ dealing with “puts” to shadow banks, the focus of much recent regulatory action; and, most importantly,

  • ❑ considering its macroeconomic, monetary and quasi-fiscal implications.

To the extent that many shadow-banking activities have valid and valuable economic and financial market rationales, regulation should not be so strict as to remove the positive aspects of shadow banking. However, this does not mean that a policy response is unnecessary, since systemic risk needs to be contained. The Financial Stability Board (FSB) articulated an agenda to deal with regulatory weaknesses, spillovers and systemic risk in shadow banking (The FSB (2012) defines shadow banking broadly as “credit intermediation involving entities and activities outside the regular banking system”. While measures of shadow banking differ considerably, the system is large, comparable in size to traditional banking, and is continuing to grow.)

This chapter will focus on forthcoming issues that appear “shadowy”. Seminal papers on shadow banking include Gorton and Metrick (2010) and Kane (2012). Prior to the global financial crisis, much of the discussion was on securitisation and upgrade of assets, including the use by banks of affiliated investment vehicles to offload credit risks (and economise on capital); credit and liquidity guarantees with too little provisioning; and investments in structured products where capital charges did not reflect underlying risks. This chapter will thus not repeat this literature and research.

Short-term wholesale funding markets continue to remain vulnerable after the Lehman crisis. In July 2013, a member of the board of governors of the Federal Reserve System, Daniel Tarullo (2013), reiterated that:

… a major source of unaddressed risk emanates from the large volume of short-term securities financing transactions (SFTs) in our financial system, including repos, reverse repos, securities borrowing, and lending transactions.

The Financial Stability Board has initiated a working group on SFTs under the rubric “non-cash collateral reuse”, which is similar to the concept of collateral velocity described in Chapter 2. This chapter will bring in the collateral velocity angle and how it impacts on the non-bank/bank nexus. In the analytics that follow, we will use “zi”, to define the non-bank funding to banki,. In an era of quantitative easing (QE) by central banks and regulatory proposals demanding more safe assets, this chapter proposes increasing collateral velocity to bridge the gap between demand and supply. We introduce central banks in the bank–non-bank nexus, since they are now (and will continue to be) a major player in the collateral market (and is discussed in depth in Chapter 4, which discusses monetary policy).

Basic Analytical Framework

Bank credit to ultimate borrowers is funded by either the equity of the banking system or the funding that non-banks (ie, households, pension funds and insurers) provide to the banking system. This is depicted in Figure 3.1 (Shin 2010). The term on the left of the equation, yi, denotes the total lending to ultimate borrowers. The term in the middle balloon denotes the total funding to the banking sector provided by non-banks (or outside claimholders). And the term on the right denotes the total equity of the banking system.

Figure 3.1Funding of Bank Credit

yi is the total claims on ultimate borrower by bank,

ei is the equity of banki

λi is the leverage of banki2

zi is the fraction of non-bank funding banki receives

The traditional view of a banking system is that total funding from non-banks (the first term on right-hand side shown in red balloon) is relatively “sticky.” In other words, it is often assumed that, non-bank funding to banks predominantly reflects households’ deposits only (or M2, a metric that measures broad money) and the stock of house-hold deposits is steady and in line with relatively slow-moving household wealth. Thus, according to earlier research, non-bank funding to banks does not vary much. Household deposits grow in line with household wealth and income – steadily.

As such, rapid increases in the aggregate volume of credit supplied through the banking system must come via increased leverage (λi), which – due to the “stickiness” of the red balloon and the stable nature of M2 – is assumed to come from increases in interbank claims. Thus, in Adrian and Shin (2010) interpretation, shadow banking is largely an interbank phenomenon.3

This view, however, ignores the significant funding that banks receive from the asset-management complex; these are not fully captured in monetary aggregates such as M2. Even when household deposits are sticky, when we introduce non-bank firms and intermediation through the shadow-banking system, both individual banks and the banking system as a whole can (quickly) lever up.4 In the US, as noted, the gross volume of funding from non-banks that was intermediated by banks may have been as high as US$25 trillion and US$18 trillion at year-end 2007 and 2010, respectively. In other words, non-banks’ funding to banks involves much more than just households and their deposits.

So, even with M2 being stable, the banking system can leverage up, not necessarily by increased interbank lending but because of the portfolio choices of the asset-management complex. Unlike short-term household funds – which are primarily in M2 liabilities – short-term investments of asset managers are primarily in the form of non-M2 liabilities. In turn, the supply of privately guaranteed non-M2 liquid assets is, by and large, a function of the aggregate volume of short-term claims. Since the money holdings of asset managers are ultimately the claims of households, it follows that households ultimately fund banks through both M2 and non-M2 instruments. It is important to note, however, that, while households’ direct holdings of M2 instruments reflect their own investment decisions, their indirect holdings of non-M2 instruments are not a reflection of their direct investment choices, but the portfolio choice and investment management techniques of their fiduciary asset managers.

Zi can be expressed as Zh + Zk,


Zh is the fraction of M2 funding that bank receives from households, and

Zk is the fraction of non-M2 funding that bank, receives from non-banks

We depict the analytical framework in Figure 3.2, highlighting the bank–non-bank nexus, which includes collateral velocity and leverage that is key to understanding shadow banking:5

  • ❑ ultimate savers (rightmost column of Figure 3.2), which include short-term household and corporate savings and long-term investors through the asset-management complex (insurance, pension funds);

  • ❑ ultimate borrowers (leftmost column), which include corporations, households and government; and

  • ❑ dealer banks, which play a central role in intermediating collateral and money flows; these dealer banks connect the non-bank space, including recent central-bank quantitative easing (QE) types of activities, and funnel collateral or money (a) between various non-banks (money market funds – or MMFs – hedge funds, pension funds, insurers, official sector accounts) and (b) from non-banks to central banks.

Figure 3.2The Financial Plumbing

1Figure 3.2 is a snapshot of “z” or the non-bank/bank nexus explained in the analytical framework. The dealer bank depicted above are active in the cross-border collateral intermediation. So “zi” is important for dealer bank “i”. The ultimate borrowers also borrow directly from commercial banks; however they are not shown in this figure as their interaction with non-banks is minimal; hence “zi” is negligible.

The rest of the chapter discusses collateral use and reuse (or velocity) and how it impacts on “zi, or the non-bank funding to banki explained above. Then the discussion moves to the shortage of safe assets. In an era of QE by central banks and regulatory proposals demanding more safe assets, this section proposes increasing collateral velocity to bridge the gap between the demand and supply. We also introduce central banks in the bank–non-bank nexus, since they have become major players in the collateral market. There are still “puts” (or taxpayer bailouts) that remain at large.

Collateral Use and Reuse

As discussed in Chapter 2, the stock of collateral and its velocity (the intensity with which it is reused) are both fundamental to understanding the financial plumbing in the shadow-banking world. The volume of collateral transactions declined over the years 2007–15 from US$10 trillion to US$5.6 trillion, while the stock of collateral declined from US$3.4 trillion to US$3.1 trillion. The stock of collateral can decline as investors become more concerned about counterparty risk, making them less willing to lend securities and causing collateral to sit safely idle in segregated accounts. It can also be affected by central-bank measures, such as large-scale asset purchases, which drain good-quality collateral from the system, or a widening of the pool of collateral-eligible assets, which increases the pledgeability of these assets as collateral to the central banks. Velocity can therefore change, like the velocity of money: it was 3 at end-2007, 2.4 at end-2010 and 1.8 at end-2015.

The collateral intermediation function of shadow banking is important within the financial system and, to the extent that it supports credit, it is also important for the real economy (although quantifying the economic importance is complex).6 When collateral use drops, financial intermediation slows, with effects similar to the drying-up of interbank markets. The velocity or reuse is an important concept and a key determinant of “zi” in this analytical framework (Pozsar and Singh 2011).

Shortage of Safe/Liquid Assets

Prior to the 2007 crisis, the private sector inundated the supply of AAA securities. The mismatch between the supply and demand for safe assets is only partly resolved by adjustments in relative prices – the rates of return on various types of assets. The reason is that the demand for safe assets and the supply of truly safe assets are relatively price-inelastic, which can make the equilibrium price of government-guaranteed safe assets very high (and their yields low or negative), creating incentives for the system to create private safe assets (Figure 3.3).7

Figure 3.3Increasing Share of AAA Securities that Came From the Private Sector (1990–2009)

Source: Bank for International Settlements (BIS)

Accordingly, a number of academics and policymakers have advocated correcting the mismatch directly by having the government at times expand the supply of safe, short-term liquid instruments to crowd out those supplied by the shadow-banking system (Ricks 2011; Gourinchas and Jeanne 2012). In their models, the government is in a better position than the private sector to issue safe assets thanks to its power to tax and suggest that any excess demand can be met by offering more short-term debt. The models in the academic paper skirt the concept of reuse of good collateral (or safe assets). This would reduce demand pressures to create unstable private assets and remove a major source of systemic risk. However, adjusting the supply of short-term government debt can come with some challenges, particularly related to debt management. Authorities may have to depart from widely accepted minimal-cost rules in debt management (Garbade 2005). By issuing more short-term paper than other considerations call for, the government would take on some interest-rate and operational risks from the private sector. There is a choice between (a) issuing more “safe assets” at a cost to debt issuance and (b) the private sector increasing “effective” supply by higher reuse (or velocity) of good collateral (ie, high-quality liquid assets, or HQLA).

There may also be other conceptual and practical limitations to the effectiveness of demand-side policies. It is unclear whether it is appropriate for the government to engage in creating financial market assets with the sole purpose of catering to a particular investment clientele. For example, this could create moral hazard in that the private sector might come to expect that the government will accommodate its demand for specific types of assets.

Some “Puts” that have Remained at Large

There are “puts”, or potential taxpayer liabilities, associated with the shadow-banking world. What are the puts and why do they continued to exist? Here we discuss the typical players in the shadow-banking literature that may access the “puts” – hedge funds, MMFs, central counterparties (CCPs) that will inherit OTC derivatives from dealer banks, and the triparty repo entities (specific to the US). Providing puts ex ante for fear that the ex post bailout might be even more expensive is a circular argument that encourages moral hazard and exploits regulatory arbitrage. The key non-banks that liaise with dealer banks were exhibited in Figure 3.2.

Hedge Funds

One source of systemic risk (and risk to the public safety net) in collateral intermediation is the liquidity exposure of dealer banks. Dealer banks routinely use/reuse/reycle some collateral obtained from customers (eg, rehypothecation from hedge funds) for their own funding. A customer withdrawal may then have liquidity implications for the dealer bank, which will have to find new sources of collateral or liquidate its own positions Runs by prime brokerage clients (typically hedge funds) demanding their collateral back was a major source instability for dealer banks in 2008 (including all standalone US investment banks, such as Bear Stearns, Lehman Brothers and Merrill Lynch), leading to large central-bank and government support measures.

Now, new regulations monitoring large non-banks are in place (in the US) and non-bank SIFIs are also being designated by other regulators. Post-Lehman, the UK’s former regulator the Financial Services Authority (FSA) – whose responsibilities are now carried out by the Prudential Regulation Authority and the Financial Conduct Authority – also came a long way in articulating the UK’s rehypothecation rules to hedge funds domiciled there.

Dealer Bank’s non-Depository Affiliate

The “puts” to the safety net are especially significant when a dealer bank is also a depository institution. This creates scope for moving risks to the depository part (Singh 2012), which subsidises the shadow-banking activities by reducing the funding cost. For example, in the US, after Bank of America (BoA) and Merrill Lynch (ML) merged, the OTC derivatives book of ML was “moved” to the depository part of the merged BoA–ML. As a consequence, taxpayers may now provide a stronger backstop to the bank’s overall derivatives position.

Such conglomeration also creates conflicts and regulatory challenges, and increases risks to the taxpayer. Since the crisis, all dealer banks have had access to central-bank liquidity facilities through affiliated commercial banks, even though the depository part can represent as little as 5% of the group’s overall balance sheet. This offers stability of funding, but increases moral hazard, as a dealer bank can shift risky assets to its bank subsidiary.

More generally, dealer banks can have incentives and abilities to increase risks in more extreme ways than commercial banks do. The Lehman crisis has made it clear that the regulation and supervision of broker-dealers was not rigorous enough and orderly resolution is a challenge. Yet, a comprehensive framework for regulating brokerdealers – one that is as well articulated as the one that exists for banks – is lacking.8 Thus, systemic risks and puts to the safety net from dealer banks will likely persist.

Money Market Funds

There is also need for progress on MMFs. Although smaller than before the financial crisis, the US money-fund industry remains systemic and fragile. It offers on-par guarantees that cannot, as the crisis has demonstrated, be supported in times of stress when asset values drop, necessitating government support. Current solutions include lowering the average asset maturity of MMFs, introducing capital requirements, requiring a floating net asset value, or NAV (as is largely the case in Europe), and using two-class claims on assets (one redeemable at par and the other contingent on the NAV).

Some changes have been made in the US. Prime MMFs with AUM of about US$1.0 trillion (at present, and likely to shrink further) will be floating NAV starting from October 2016; however, the government MMFs with about US$1.6 trillion AUM will continue to valued at par NAV but with more stringent investment criteria. This is a major change in recent years and reduces some (but not all) risk to taxpayers. In the past, MMFs have been a significant source of systemic risk: in the US, the government was forced to step in to limit the spillovers from a run, as happened in 2008 (McCabe et al 2012). Interestingly, constant NAV in Europe is allowed for only short-term MMFs (ESRB 2012); these MMFs operate with a very short weighted average maturity (WAM) and weighted average life (WAL) – the logic is sound that anything beyond short term should not be constant or “par”.

Qualified Financial Contracts

QFCs take the form of derivatives and repos. Prevailing legal rules, such as the “safe harbour” provision, allow some QFCs to be exempt from “automatic stay” during bankruptcy, ie, they are prioritised in reorganisation because they are deemed to be too interconnected with financial markets and thus too disruptive to tinker with. This exemption reduces market discipline and effectively subsidises the contracts’ counterparties (dealer banks and the wider shadow-banking system) at a cost to other creditors and the public safety net. While there is little to suggest that legal changes are imminent, recent studies highlight that the exemption status might not be economically justified (Summe 2011; Bolton and Oehmke 2011; Bliss and Kaufmann 2005). Lately, in a working group initiated by ISDA, major banks may be amenable to a temporary stay on SFTs (although buy-side is reluctant to dilute any rights due to their fiduciary duty to their clients; some jurisdictions, such as Hong Kong, are also not in sync with this move).

OTC Derivatives Move to CCPs

The G-20 Pittsburgh meetings in 2009 decided that a critical mass of dealer banks’ derivative-related risks will be moved to CCPs (which were until then viewed under the rubric or payment systems). This is a huge transition: primarily to move the risk from OTC derivatives outside the banking system. These new entities may also be viewed as “derivative warehouses”, or concentrated “risk nodes”, of global financial markets.9 On average, each of the key dealer banks carried about US$100 billion of derivatives-related tail risk around Lehman’s demise – this is the cost to the financial system from the failure of a dealer bank, where tail risk is measured by the “residual” derivative liabilities of a dealer bank (ie, after netting and collateral, see Chapter 6). Yet, instead of addressing the derivatives tail risk, the present regulatory agenda is focused on offloading all (or most) of the derivatives book to CCPs. They have also been incorrectly compared to utilities. Recent research suggests that the move to CCPs may not be superior to the bilateral clearing world pre-Lehman, if we calibrate the arithmetic of netting loss, default funds etc (Ghamami and Glasserman 2016).

Triparty Repo

A distinct part of the collateral intermediation process, the triparty repo (TPR) market, can present a different set of systemic risks. TPR is a major source of wholesale funding for banks and dealer banks, especially in the United States, where volumes approach US$1.6 trillion as of end-2105 (down from US$2.8 trillion in 2007). In the US TPR, one of the two intermediaries (either JPMorgan or Bank of New York) facilitates a repo operation between counterparties that are the dealer banks. Reforms to TPR are in motion but so far the intraday position is risk to the intermediaries – and the reason why the Bank of New York (BoNY) is designated a SIFI. This highlights the “put” faced by taxpayers. The TPR market in the US differs from the bilateral pledged-collateral market – the latter is truly mark-to-market and the crux of this book. In continental Europe and the UK, TPR activity has increased in recent years to roughly €1.1 trillion, largely due to multinational companies keeping money oversees and recent counterparty risk concerns regarding large banks. It takes place among four agents: Euroclear, Clearstream, the BoNY and JPMorgan – see Chapter 8.

Policy Implications of Shadow Banking

Shadow banking is highly procyclical, which may have adverse real-sector consequences. For example, secured lending and repos rely on mark-to-market prices and margins/haircuts that adjust over the financial cycle; in the extreme, some collateral may become unacceptable during periods of turmoil. Also, shadow-banking services enable greater financial-system interconnectedness, which helps reduce idiosyncratic risk through diversification but also exposes the system to spillovers in the event of large shocks. A proposal to reduce procyclicality via ex ante haircut schedules is not clear; this will be impossible to implement in bilateral collateral agreements as they can distort the price-setting for pledged collateral – the essence of financial plumbing! Credit support annexes in the OTC derivative contracts, or master agreements that underpin cross-border repo and securities lending, are privately negotiated bilateral agreements that regulators should not tamper with. Such contracts include the “legal wheels of title transfer” and are designed to make financial collateral akin to money so that markets settle accounts/margins by “cash or cash equivalent”.

Shadow banking is likely to have important interactions with monetary policy. Just as interest-rate transmission can be impaired if the banking system is weak, so do the broader channels of monetary policy transmission depend on well-functioning capital markets, including shadow banking. The state of private, safe asset supply and the stock and velocity of collateral can therefore affect monetary policy transmission, with macroeconomic consequences. And monetary policy can affect risk taking in shadow banking. When the interest rate is low, a steeper yield curve that increases the payoff to maturity transformation and risk-taking can lead shadow banking to expand rapidly, potentially leading to financial fragility.

And, during crises, shadow banking may require public support, leading to fiscal implications. As the rate cycle will increase from near zero rates, a higher monetary policy rate will also increase quasi-fiscal costs (eg, the Fed and the Bank of England, which provide interest on excess reserves). Unless the systemic risks in shadow banking are addressed, these contingent liabilities (or “puts”) will remain in place, with perhaps larger actual costs in future crises.

Addressing the shadow-banking system is a work in progress for regulators and policymakers, and research is yet to catch up fully with the issues. However, synonymous with the assumption that “shadow banking” is a pejorative term, much of the collateral intermediation is assumed to be risky by financial regulators. Current regulatory approaches are actively pushing banks away from short-term, secured, wholesale funding markets and incentivising them to issue more deposits and term funding. The likely result would be that riskier activities move outside the banking system (proprietary desks, hedge funds, OTC derivatives and CCPs, to name a few), and into the shadow-banking world. Thus, going forward, understanding and correctly mapping the shadow-banking system will become even more important for policymakers (see Annex 3.1).

Annex 3.1: Flow-of-Funds Data and ITS Limitations

Banking sector and other financial data is captured in flow-of-funds (FoF) statistics such as those produced by the Fed. Yet aspects that cover the banking sector and its nexus with the non-banks are not covered by the FoF statistics. This annex attempts to highlight some of the salient aspects of the FoF statistics of the US to show that, even in mature markets such as the US, there are “data gaps” in financial statistics that need to be complemented by a rigorous analysis of offbalance-sheet statistics, and linkages with other sectors that are outside the regulatory perimeter.

First, SIVs (special-investment vehicles) and other off-balance-sheet entities, were considerable in the run-up to the 2007–8 financial crisis. Although they are picked up in the FoF data, data is aggregated. Presently, there is no way of tracing back to the banking sector off-balance-sheet liabilities such as asset-backed commercial paper (ABCP) and money funds, via FoF data. Hedge funds’ position and ownership of financial assets are buried in FoF data’s “household” sector. By aggregating and netting across all banks, the FoF data loses relevant information. For example, security lending on Page L130 of the FoF data is shown “net” in line 20, and thus would not highlight large positive build-up in, say, Bank X and a negative build-up with, say, Bank Y. Thus the FoF data has limitations for -early-warning signals.

Second, derivatives data is also difficult to discern in the FoF data. Financial statements do not provide the undercollateralisation (or margin shortfall) of derivative positions. For some of the recent members of the “banking community”, Goldman Sachs has most of its plain vanilla derivatives books in the bank part of the Goldman Sachs bank holding company, for example, while its equity and commodities derivatives are conducted out of the brokerage subsidiary. Most of the (notional) derivatives for Morgan Stanley were still being conducted outside the commercial bank. The FoF accounts presently reflect only the flow of savings and investment of an economy. Derivatives unbundle risks associated with the securities that transmit the flow of savings and investments. To adequately track the workings of modern financial systems, FoF statistics will ultimately have to include “satellite” accounts that track the flow of risks and collateral.

Third, more granularity is needed in the breakdown in the types of short-term money market instruments. FoF data uses the term “open market paper” to capture money market instruments such as financial, nonfinancial and ABCP, Treasury bills and agency discount notes. Not only is the breakdown of short-term instruments not granular enough, but it is impossible to track the detailed holdings of short-term instruments (ie, money funds’, securities lenders’ or corporate treasurers’ holdings of short-term investments). In summary, instruments of maturity transformation and the holders of risks related to maturity transformation are close to impossible to track through FoF data.

Fourth, bank holding companies such as Citibank, JPMorgan, Deutsche Bank, Goldman Sachs and Morgan Stanley are not fully reflected in banking statistics. FoF data shows all elements of the holding company (bank, dealer, asset manger, etc) but “tears up” Holding Company X’s balance sheet and then aggregates all banks in one sheet, all dealers in another sheet etc. This aggregation leads to a loss of the overall picture of the holding company; hence the need to go back to the 10Q/10K (ie, abbreviations in the US for quarterly and annual financial reports, respectively) to see the build-up of all business positions of the bank holding company from its various components under one roof.

Overall, non-bank linkages with the banks are not fully captured in FoF statistics. Thus FoF data needs to be augmented by other information that is usually buried in the footnotes to financial statements.


Written for the Reserve Bank of Australia’s Funding and Liquidity Conference, Sydney (August 2013).

This notation does not fully accord with current accounting and regulatory conventions. For example, from a regulatory point of view, until Basel III is implemented, leverage refers mostly to on-balance-sheet leverage. According the definition of Basel III, several off-balance-sheet items will come on the balance sheets by 2017.

Shin and Adrian (2010) note that “M2 [. . .] is a good proxy for the total stock of liquid claims held by ultimate creditors against the financial intermediary sector as a whole” and later demonstrate that M2 has been slow-moving or stable over time, expanding “by a factor of 2.4 since 1994”. Shin (2010) notes that “the total liabilities of the banking sector to the household creditors can be expected to be sticky, and would be related to total household assets. [. . .] For the purposes of short-term comparative statics, we could treat it as a constant.”

Leverage is typically measured on a gross basis and interbank lending on a net basis. As an example, Bank A wants to buy a million dollars of securities from a non-bank person and gets financing from Bank B (on the basis of the collateral of the securities), which refinances from Bank C, which in turn refinances with Bank D, which obtains funding through an ultimate non-bank saver (a household or mutual fund). Assets of Banks A, B, C and D go up by US$1 million each, for a total of US$4 million – gross interbank lending/borrowing of US$3 million and financing from non-banks of US$1 million. Since capital has not changed, leverage goes up (banks assets go up by 4). Assume “z” is the proportion of non-bank funding to the banks; thus, the total bank financing goes up by 4 million of which only 25% is from non-banks.

There are other commercial banks (not shown in Figure 3.2) that are not active in collateral intermediation but connect the ultimate savers to ultimate borrowers via syndicated loans, letters of credit and the traditional banking services. These are not the globally systemically important financial institutions (G-SIFIs), and span the small, medium and even global nondealer banks. In the analytical framework described above, the business operations of these commercial banks (generally) do not interact with the non-bank via derivatives, securities lending, repo agreements or prime-brokerage activities. Hence the “zi” for commercial bank “i” will not be significant. However, the ultimate borrowers (yi) will borrow from both types of banks.

For example, a pension fund adept in securities lending may augment returns to its pensioners in the real economy. Another example: a hedge fund may bid for an IBM bond issue since it has funds via its prime broker (in lieu of collateral posted). A higher number of bidders lowers IBM’s cost of bond issuance, thereby benefiting real sector. Markets will use money or collateral, whichever is “cheapest-to-deliver” when settling debits/credits. This makes it difficult to quantify what fraction of collateral goes to real economy because both money and collateral are pooled together.

As documented by Duffee (1996) and this study updated by Greenwood, Hanson and Stein (2012), investors will pay a “premium”, ie, accept a lower yield, for some types of T-bills, as they offer a preferred combination of safety and liquidity.

In the United States, the Dodd–Frank Act gives authorities powers to move a systemically important broker-dealer under the supervision and regulation of the Federal Reserve. This may strengthen supervision by making it more comprehensive, but it does not address how to effectively regulate dealer banks – that is, a broker-dealer that is an integral part of a banking group. (Note that in the United States and elsewhere, while the safety net can extend to the whole SIFI, the broker-dealer operations can dwarf its banking part; for example, deposits of US and EU SIFIs – that is the bank part – are often less than a third of the overall assets of the SIFI in the bank holding company.) Similarly, while Dodd–Frank enables an orderly liquidation of a dealer bank by the Federal Deposit Insurance Corporation, the precise processes have neither been fully articulated in theory nor tried in practice. At the same time, Dodd–Frank has tightened the rules of lender-of-last-resort support to non-banks (Tuckman 2012). Individual firm assistance is no longer available, although broad-based lending programmes are still allowed in systemic crises, subject to approval by the Treasury secretary.

There are many proposals on trying to unwind SIFIs; it is a difficult (if not an impossible) task. So creating new SIFIs such as CCPs should be backed by sound economics.

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