Regulation

Trudeau’s proposed speculation tax

Published by Anonymous (not verified) on Thu, 26/09/2019 - 11:45am in

I’ve written a blog post about the Trudeau Liberals’ recently-proposed speculation tax on residential real estate owned by non-resident, non-Canadians.

The full blog post can be accessed here.

Trudeau’s proposed speculation tax

Published by Anonymous (not verified) on Thu, 26/09/2019 - 11:45am in

I’ve written a blog post about the Trudeau Liberals’ recently-proposed speculation tax on residential real estate owned by non-resident, non-Canadians.

The full blog post can be accessed here.

Financing hydrogen iron

Published by Anonymous (not verified) on Sun, 15/09/2019 - 1:18am in

Tags 

Regulation

We know how to make the electricity supply renewable. We know how to make land transport electric. Both are on track. But there are four problem industries where things are not so clear.

These estimates are not all for the same year and not strictly comparable, but they are good enough to make the point that to reach net zero emissions, the four sectors (together 20% of global fossil emissions) cannot be ignored.

The challenges are distinct but they have common features.

  1. Very plausible
    technological pathways exist to decarbonise. But these are not
    mature, and for the moment they are far more expensive than BAU.
  2. There is no
    guarantee or strong expectation that technical progress will ever
    eliminate the cost barrier, in contrast to electricity and land
    vehicles.
  3. The industries
    are typical of modern capitalism: they are international and
    oligopolistic, with a lot of trade, a handful of large companies,
    and a myriad of small ones.
  4. Their products and services rarely have plausible substitutes. (We shall see later on why this matters).

Points 1 and 2 mean that the issue for public policy is not R&D (pace all the Democratic presidential hopefuls) but early deployment.

Recall how we got to cheap wind, solar and batteries. It wasn’t a carbon tax, since that does not exist anywhere in the pure form. Partial cap-and-trade exists in the EU, but it has only just started to bite, after giveaway initial allocations. It was done by subsidies for early deployment to create economies of learning and scale:

  • In the USA, tax breaks for wind, solar, and electric cars; renewable obligations at state level.
  • In Europe and China, tax breaks, subsidies, and regulatory privileges for electric cars.
  • FITs and ringfenced auctions for wind and solar generation in Germany, other European countries, China and India.

The costs of FITs have been large in the past, though the cumulative liability (in Germany for instance) has now almost stopped growing as the few surviving FITs are near market rates. Well worth it of course, especially if you aren’t a German consumer.

The same principle holds for our four problem industries. Carbon taxes are politically toxic, and a coordination nightmare in globalised industries. So what’s the workable second-best kludge?

I’d like to float a possible solution. I’ll take steel as the example. The principle extends to the others ceteris paribus.

Steelmaking has two stages. Step 1 is reducing the iron oxides found in nature to pig iron, typically by heating with coke in a blast furnace, a Han-dynasty Chinese invention. Take hematite. In multiple chemical steps,

2.Fe2O3+ 3.C → 2.Fe + 3.CO2

POSCO blast furnaces at Pohang, Korea

Step 2 converts the brittle pig iron to more versatile steel by adding a small percentage of carbon in an electric arc furnace. This can also be done with scrap iron and steel, 25% of all steel production today. Since Step 2 can be done with renewable electricity, the carbon emissions problem is all about Step 1, pig iron.

Electric arc
furnaces are distributed all across the globe in industrial cities
with a good supply of scrap metal. Blast furnaces are found in
monstrous coastal integrated plants, run by the largest companies.
The 15 largest steel producers:

Source: World Steel Association

These 15 are
responsible for a third (32%) of global steel production, but more –
I suppose over 40% – of the new pig iron we are interested in.

The technology for decarbonization of ironmaking is direct reduction (DRI). Iron ore is heated up with natural gas (CH4), reformed to a mixture of carbon monoxide and hydrogen :

3.Fe2O3 + 6.CO → 6.Fe + 6.CO2

and 8.Fe2O3 + 24.H2 → 8.Fe + 24.H2O

This is done at
reasonable scale today (100 mt/yr), mainly in India.

However, the process works fine just with the hydrogen:

Fe2O3 + 3.H2 → 2.Fe + 3.H2O [equation corrected]

Two pilot plants for hydrogen DRI are being built in Sweden and Austria by SSAB and Voest respectively with EU research funding. ArcelorMittal are also building a pilot hydrogen DRI plant in Hamburg. These are major and long-established steelmakers; the process almost certainly works.

Technically, but not yet financially. The price of catalysed hydrogen will have to drop a lot for that to happen. The steelmakers are saying “we can do it, but it’ll cost you”. None will move at a large scale – decommissioning a blast furnace is an expensive decision – without an incentive. Collective action problem! So we need early deployment subsidies once again.

Here’s my Cunning Plan (Baldrick™). There are many proposals floating around for fiscally neutral general carbon taxes: the revenue is typically rebated to low income taxpayers. The main objections to this are (a) it’s still a carbon tax (b) it’s too clever by half. But also (c) nobody knows how effective it would be. Relative prices shift (good) but you are also throwing in an uncertain income effect on those who don’t get the rebate, and you are hoping for a cultural nudge too.

HOWEVER these
difficulties mostly go away with a sectoral levy-and-rebate
scheme.

The early German FITs for renewable energies tried to remove the incentive to waiting for prices to come down aka the penalty for early adopters. The aim was to maintain a roughly equal ROI over time, steadily lowering the FIT rate in line with installation prices. It wasn’t perfect but basically worked. In particular, it was seen as fair, and SFIK there is no significant resentment of the early adopters who got the high FITs.

My scheme would try to identify the current excess cost over 10 years of a hydrogen DRI plant, estimate the likely total volume of subsidised investment, and set subsidy and matching levy rates on carbon-emitting iron. As hydrogen gets cheaper, the rates would fall. You would need a working fund or equivalent budget guarantees to cope with the inevitable errors.

It’s
fiscally neutral for the industry. But there are no free lunches.
Hydrogen steel is more expensive to make than the carbon-emitting
variety. Consumers will pay a slightly higher price for all steel to
fund the transition. Most of them will hardly notice. The lack of
substitutes for steel means that the industry as a whole will not
lose significant business to say aluminium.

The scheme depends on negotiation with the leading producers, that is my top 15, but it should be open to anybody. For holdouts and smaller players, governments would have to be prepared to impose the levies as taxes. It also presupposes a lot of coordination between governments. This does not have to be universal. You need a “coalition of the willing” covering a significant proportion of the industry (as a minimum China, India and the EU, if possible Japan, Korea and the USA), and prepared to impose border taxes on holdouts.

Still
too clever by half? Maybe. What’s your alternative?

Exercise for the next session of the Public Policy seminar: work up a proposal on the same lines for aviation, shipping or cement, with the pros and cons. Tip on shipping: flag-of-convenience states are tiny and in no position to stand up to serious diplomatic pressure from big players who mean business. You can stop well short of sending an aircraft carrier to intimidate the Marshall Islands.

BTW, if my scheme works in more than one sector, you are growing a global carbon tax from the bottom up.

One clean beach

Published by Anonymous (not verified) on Mon, 19/08/2019 - 12:43am in

Tags 

Regulation

No pretty photograph for this one. How can you take a snap of something that isn’t there?

Plastic litter on my local beach, that’s what.

I moved to Spain 15 years ago. My beach walks were interrupted by regular collections of litter, almost all plastic of one sort or another: drinks bottles, throwaway shopping bags, formless lumps of polystyrene, broken tangles of fishing net. It was densest along the shoreline, so jetsam (nice word: its counterpart flotsam is floating junk).

Recently I have had to leave my spandex Supergramps suit at home. There is hardly any to collect. On reflection, the change has been slow, though I’ve only just noticed it. Why has this happened?

The municipality has been putting an effort into the beach. It’s a standard policy in seaside resorts to try to move upmarket to catch tourists with more to spend, so there are more chiringuitos, beach playgrounds, access ramps for wheelchairs, free showers, dustbins and so on. The litter disappearance is no doubt partly down to the Alcaldía (among many Spanish words in administration and commerce with Arabic roots) putting more effort into beach cleaning. However, I’ve very rarely seen the crews: I suppose they do their work earlier in the morning than I get up. The thing is, the beach is still litter-free at 8 in the evening, time enough for dedicated louts to cover it in rubbish. Much less is being dumped now. Something else is going on.

There is a pretty theory of tipping points that might explain it, a variant of “broken windows” : the idea is that while many people will add their trash to an already polluted environment, few will be the first in a clean one. So Spanish beaches and South Bronx streets alike have two equilibria: Switzerland and slum. The hypothesis is that the cleanup has been vigorous enough to flip the beach to the former. Alternatively, the sight of eccentric foreigners ostentatiously picking up plastic bottles has started a social meme of approval and disapproval, which has by itself reached the general tipping point. I don’t buy this as a significant part of the story, flattering though it is.

The big problem with the tipping-point theory is the sheer variety of social groups who would have had to flip at the same time. Beach litter can be generated by:
Beach users:

  • Spanish local residents
  • Foreign local residents (subdivided by nationalities)
  • Summer Spanish tourists
  • Foreign summer tourists (subdivided by nationalities)

Sea users:

  • local Spanish inshore fishermen\
  • local recreational boat owners (we have a large marina)
  • commercial shipping in the channel a few miles offshore (subdivided into cargo and cruise ships)

Possible river users:

  • local Spanish farmers

The clean beach requires similar and parallel action by all these groups (well, I’m not sure about the farmers). They do not interact much with each other, and in one case – the deep-sea shipping crews and passengers – not at all with the landlubbers. The kind of mutual observation of behaviour and exercise of social pressure required by the tipping point story can only happen on a very weak scale.

The rival theory is that this simply reflects a broad, and pan-European, change in sensibility. Dropping litter used to be OK, except for anal-retentive parents, cops and teachers, now it isn’t. Compare smoking. Could someone please mine Twitter for the spontaneous thoughts of teenagers on plastic and litter? I suspect these have gone from “Eek, spoilsport crumblies” to “Eek, terminally uncool yobs”. Note also that it has required both top-down public policy (beach cleaning, plastic bag fees) and bottom-up movement in civil society.

Trivial? Not if you can generalise it it to the much bigger problem of plastic pollution in general. The cumulative global stock of unrecycled plastic waste has been estimated at 6.3 billion tonnes. The half-life in a landfill varies from 10 to 1000 years. Arctic snow is awash in microplastic particles, and plastic rubbish has been found at the bottom of the Marianas Trench.

As a tiny first step, many European governments have introduced nominal minimum charges by shops for plastic bags. A 5c bag fee is the ultimate test of nudge theory, but it does seem to be working. My local roast chicken takeaway has, without legislative pressure, also introduced the more expensive (15c) option of a nice paper carrier bag: it finds a good many takers. (Not me, I take my own Supergramps insulated bag.) Is the mechanism here that eco-virtue reduces guilt for resorting to a takeaway rather than cooking a Real Meal at home? If that’s so, still fine by me.

Memo to self: research a proper blog post on the cost of sustainable packaging. Meanwhile, my clean beach offers hope that the needed change is doable.

All right, a photo of my ordinary beach at sunset. Nothing special, but I like it.


The Growth of Shadow Banking and State-Finance Relations

Published by Anonymous (not verified) on Fri, 16/08/2019 - 1:04am in

by Matthias Thiemann* How can we understand the growth of a system of credit provisioning outside of the realm of bank regulation since the 1970s which linked non-banks and banks in a convoluted system of market-based banking, securitization and wholesale … Continue reading →

At the New York Fed: Research Conference on FinTech

Published by Anonymous (not verified) on Fri, 19/07/2019 - 9:00pm in

Tags 

Regulation

Alan Basmajian, Brad Groarke, Vanessa Kargenian, Kimberley Liao, Erika Ota-Liedtke, Jesse Maniff, and Asani Sarkar

 Research Conference on FinTech

Financial technology (“FinTech”) refers to the evolving intersection of financial services and technology. In March, the New York Fed hosted "The First New York Fed Research Conference on FinTech” to understand the implications of FinTech developments on issues that are relevant to the Fed’s mandates such as lending, payments, and regulation. In this post, we summarize the principal themes and findings of the conference.

FinTech Advisory Group


In his opening remarks, Kevin Stiroh, head of the New York Fed’s Supervision Group, noted both the benefits and risks that FfinTtech poses to the economy of New York City and the surrounding region. Underscoring the need to think more systematically about FinTech, Kevin announced the creation of the New York Fed’s FinTech Advisory Group, a body that will provide the Bank with a more complete picture of the rapidly evolving FinTech landscape.

FinTech Themes

In the opening panel, panelists traced the emergence of many FinTech companies to the aftermath of the financial crisis of 2007-08, a period when incumbent financial firms became distressed and cloud, blockchain, and mobile technologies gained prominence.

The panelists generally agreed that FinTech firms and incumbents both compete and cooperate with one another. As FinTech firms continue to grow by introducing new services, incumbents are upgrading their technology and collaborating with these new companies. However, as the panelists stressed, such initiatives are only “innovative” to the extent that they provide value to clients. Blockchain technology, for example, has sparked a lot of hype, but clients remain skeptical about its value. As a remedy, the panel recommended test-driving specific use cases currently being implemented to understand what clients value.

The panelists also compared China’s FinTech sector, which has become a dominant part of the Chinese financial system, to its U.S. counterpart, which remains a more marginal player in the U.S. financial sector. They noted that while U.S. FinTech firms generally originated in the regulated financial services industry (which limited their expansion), their Chinese counterparts moved from the technology sector into financial services (which facilitated their expansion).

Banks and FinTech Lenders

The next session featured presentations on four academic papers related to FinTech credit markets. The first paper notes that the costs of financial intermediation have not decreased for more than a century, emphasizing the importance of FinTech in potentially lowering these costs. The second paper finds that FinTech firms provide loans to rich and creditworthy borrowers, rather than to borrowers excluded by traditional banks, as is widely believed. Supporting evidence shows that these borrowers were using FinTech loans to consume rather than to consolidate debt, potentially making them vulnerable to default. The third paper argues that peer-to-peer (P2P) lenders displace bank loans, especially those provided by small banks. The last paper shows that P2P lending in China has been used for regulatory arbitrage. In 2013, authorities in many Chinese cities required a higher minimum down payment for mortgages on second homes. The author found that investors circumvented the regulation by borrowing the additional amount from P2P lending platforms.

In discussions following the presentations, it was pointed out that FinTech lending is part of a broader increase in lending by shadow banks, some of which were early adopters of FinTech lending platforms. Concerns were raised that shadow banks might cause financial instability since they lack a deposit-funding base and have limited balance sheet capacity. It was further noted that whereas Chinese P2P lenders are powerful, Wall Street has soured on U.S. P2P lenders, which are viewed as consolidators of subprime debt. It was conjectured that P2P lenders in the United States might shift to small and middle market lending, where banks are not efficient lenders due to weaker technology.

Economics of Blockchain, Payments, and Tokens


In the third session, three papers on the role of blockchain and tokens in payment systems were presented. The first paper distinguishes between tokens that verify the legitimacy of an asset and accounts that verify a payer’s identity as an account holder. This distinction has implications for counterfeiting. In electronic token systems, a nonbank verifier only checks whether the token has been spent and is not responsible for theft, but a bank bears additional liability for misidentifying parties and allowing unauthorized transfers from an account to proceed. Regarding theft, a fraudster that successfully compromises an account likely has access to the entire account balance. In contrast, a fraudster that accesses a token will only have the token.

The second paper highlights that “proof-of-work” payment blockchains, such as Bitcoin, face limited adoption as an inescapable economic outcome of their technical design. Higher transaction demand on these blockchain networks results in higher fees since their ledger space is artificially constrained. Higher fees, in turn, prolong payment confirmation times. A permissioned blockchain may serve as a viable alternative to Bitcoin blockchains.

The third paper studies India’s 2016 demonetization program, which caused a large, temporary decrease in the amount of currency in circulation and induced increased adoption of electronic payments. This episode shows that network effects are important in driving the adoption of new payment systems: districts with larger initial adoption of electronic payments also had greater adoption after demonetization.

In the ensuing discussions, it was argued that the self-limiting nature of cryptocurrencies such as Bitcoin inhibits their success, given the importance of network effects in payments. By contrast, mobile payment systems appear to benefit from network effects. It was suggested that the public sector might facilitate the switch to new payment arrangements. The settlement of digital assets trading on a P2P platform using tokens was also discussed. One proposal that came up was for the payments leg of the settlement process to be tokenized. It was suggested that to drive adoption, a mutual model—where owners (or members) are also the users—might be useful.

New Regulatory Tools

The fourth session featured two papers that propose new regulatory metrics using machine-learning approaches. The first paper argues that the length of a regulation is an insufficient measure of its complexity. For example, the length of Basel III was more than twenty times that of Basel I, suggesting it is that much more complex. However, a short phrase could be more complex than a longer phrase if it is harder to interpret. As an alternative, the author proposes treating regulation as an algorithm, and measuring the complexity of the algorithm. The second paper examines a general framework for analyzing large-scale textual data using semantic word vectors that infer the meaning of a word by the context of the words around it. The outputs are a set of textual factors—words and their relative frequency distributions—that can be used in regressions. The authors demonstrate the utility of their methodology by applying it to forecasting asset returns and macroeconomic outcomes.

The subsequent discussion noted that the algorithmic complexity measure would not apply to regulations that are not intrinsically algorithmic. Also, the robustness of long-term textual analysis was questioned since the meaning of words changes over time. In further discussion, the potential benefits of using technology to facilitate compliance with regulations (known as RegTech) was emphasized, especially for smaller firms, due to the fixed costs of compliance.

FinTech Research Agenda


The conference concluded with a discussion of the research agenda in FinTech. One panelist discussed his experience using a new protocol when editing a special FinTech issue of the Review of Financial Studies. He noted that FinTech submissions came from more junior researchers than is typical and mostly focused on P2P lending, big data, and blockchain. He recommended that FinTech researchers link to existing research, recognize international dimensions, and establish interdisciplinary collaboration. Another panelist proposed an integration of data science, econometrics, machine learning tools, and signal processing. Such an integration could potentially yield new results—for example, on non-linear risk premia. A third panelist considered three characteristics of big data: large size, high number of dimensions, and complex structure. Large amounts of data can mitigate selection bias in small samples and provide new economic signals. For high-dimensional data that must be processed through machine-learning techniques, an economist may utilize an alternative data vendor or collaborate with data scientists. While these novel techniques may reveal new patterns in the data, deriving economic conclusions from the results remains a challenge.

Alan Basmajian is a senior associate in the Federal Reserve Bank of New York’s Financial Services Group.

Brad GroarkeBrad Groarke is an associate in the Bank’s Supervision Group.

Vanessa-KargenianVanessa Kargenian is a cybersecurity/IT bank examiner in the Bank’s Supervision Group.

Kimberley-LiaoKimberley Liao is a senior associate in the Bank’s Supervision Group.

Erika-Ota-LiedtkeErika Ota-Liedtke is a supervisory development analyst in the Bank’s Supervision Group (seconded to the Policy Planning Office of the Executive Office).

Jesse-ManiffJesse Maniff is a payments system research senior analyst at the Federal Reserve Bank of Kansas City.

Sarkar_asaniAsani Sarkar is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:

Alan Basmajian, Brad Groarke, Vanessa Kargenian, Kimberley Liao, Erika Ota-Liedtke, Jesse Maniff, and Asani Sarkar, “At the New York Fed: Research Conference on FinTech,” Federal Reserve Bank of New York Liberty Street Economics , July 19, 2019, https://libertystreeteconomics.newyorkfed.org/2019/07/at-the-new-york-fe....




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

How Do Large Banks Manage Their Cash?

Published by Anonymous (not verified) on Wed, 17/07/2019 - 9:00pm in

Jeffrey Levine and Asani Sarkar

Second of two posts

How Do Large Banks Manage Their Cash?

As the aggregate supply of reserves shrinks and large banks implement liquidity regulations, they may follow a variety of liquidity management strategies depending on their business models and the interest rate differences between alternative liquid instruments. For example, the banks may continue to hold large amounts of excess reserves or shift to Treasury or agency securities or shrink their balance sheets. In this post, we provide new evidence on how large banks have managed their cash, which is the largest component of reserves, on a daily basis since the implementation of liquidity regulations.

What Determines a Large Bank’s Cash Holdings?

Banks primarily hold liquid securities to meet ongoing operational funding needs and cover sudden liquidity needs in periods of stress. A bank’s business model determines its services and client relationships and, in turn, its types and amounts of funding. We distinguish between three business models:

  • Universal banks, such as JPMorgan Chase, engage in a diverse range of activities including retail, commercial, and investment banking;
  • Trust banks, such as Bank of New York Mellon, specialize in investment services and asset management for institutional clients; and
  • Legacy broker-dealers, such as Goldman Sachs, originated as investment banks before becoming bank holding companies during the financial crisis of 2008.

The Liquidity Coverage Ratio (LCR ) requires banks to publicly report their prospective net cash outflows in times of stress over a thirty-day calendar period, by balance sheet category. These quarterly reports provide insight into banks’ liquidity management decisions. The chart below reports the average share of gross outflows of the eight largest banks (G-SIBs) from key liability categories (the first bar), from 2017:Q2 (the first quarter when banks were required to report data) to 2018:Q2. The chart also reports the average shares for banks with each of the three business models. We focus on gross outflows to highlight the size of business activities, although the LCR calculation uses the net outflows (that is, gross outflows net of cash inflows).

How Do Large Banks Manage Their Cash?

We discuss below the average gross outflow share of each liability category for all GSIBs and for the business model type with the largest share in that category.

Unsecured wholesale funding, such as unsecured debt and institutional deposits, averaged about 30 percent of total gross outflows across all GSIBs and about 64 percent for the Trust banks. Banks hold cash against these liabilities because institutional deposits can be withdrawn quickly and maturing debt has to be replaced or renewed. Trust banks also hold operational deposits acquired in the process of providing financial services, such as clearing and settlement of securities, to their institutional clients.

Secured wholesale funding, such as repurchase agreements or “repos” and securities lendings, comprised about 23 percent of total gross outflows for GSIBs and 40 percent for broker-dealers. Broker-dealers are heavy users of secured funding as they are securities market makers and also assist clients in funding trading positions. Notably, the public LCR data show that broker-dealers report large cash inflows from secured funding—for example, by lending cash through reverse repos—as part of their net cash outflow calculations.

Retail deposits, such as brokered and transactions deposits and certificates of deposits, were 19 percent of gross outflows for GSIBs and 30 percent for Universal banks, which tend to have large retail banking operations that fund much of their liabilities. Although retail deposits are insured by the FDIC and historically have remained stable even in crisis, banks hold a buffer against them because they can be withdrawn on demand.

Derivatives and commitments were 18 percent of gross outflows for GSIBs and 20 percent for Universal banks. A bank may act as market maker for its derivatives clients or it may use derivatives to manage its own risk. Commitments include credit and liquidity facilities that provide committed lines of credit to customers. Cash is needed, for example, to meet margin calls on derivatives positions or drawdowns of lines of credit.

Contingent funding was 10 percent of gross outflows for GSIBs and 23 percent for broker-dealers. These are bank commitments related to acquisitions or lendings that are contingent in nature, since customers often use them for short periods before replacing them with other financing sources.

Daily Cash Management Strategies of Large Banks

The public reports provide snapshots as of the end of each quarter. To provide insight into banks’ daily cash management strategies, we use daily data from regulatory reports from 2016 to 2017 on liquid assets of the commercial banking subsidiaries of GSIBs. The cash balances of GSIBs are fairly stable but with some daily variation.

To capture the cash dynamics, we use a statistical model to estimate a bank’s desired level of daily cash holdings, as a function of several factors. These factors include the anticipated amount of high quality liquid assets or HQLA that banks hold to abide by liquidity regulations and internal liquidity stress tests. The estimated level also depends on the composition of their gross “stress” outflows (that is, the expected outflows in a stress situation), as previously discussed. Finally, the opportunity cost of holding alternative liquid assets (for example, the interest rate on Treasuries or repo relative to excess reserves) also matters.

A bank’s actual and estimated desired daily cash holdings will vary due to funding shocks and other unanticipated changes in balance sheet items. We assume that, when this happens, banks plan to gradually move back to implicit desired levels. Our statistical model enables us to simultaneously estimate both the desired level and how quickly banks move back to it when hit by funding shocks.

Do banks manage cash as if they have a long-run desired level that they adjust to?

The chart below indicates that the largest banks appear to manage their actual cash levels (gold line) close to the desired level (blue line). How strictly an individual bank manages cash to its desired level depends on its business model. For example, for banks with more capital markets activities, cash levels are more susceptible to market price movements and daily cash balances are more likely to veer from the desired level. On average, however, their cash balances appear quite stable. When daily cash balances deviate from their desired levels (red line), banks gradually return to their desired levels over a period of six to seven days.

How Do Large Banks Manage Their Cash?

Evolution of the Demand for Reserves by Large Banks

To what extent can different liquid assets be substituted? We find that desired cash balances vary inversely with a bank’s holdings of U.S. Treasury securities, which suggests that banks are willing to substitute cash with Treasuries to some extent. A few banks also substitute agency securities for cash. On the liabilities side, we find that banks hold cash buffers against stress outflows from certain secured and unsecured funding categories. Overall, these results suggest that large banks are likely to change their mix of liquid assets and liabilities holistically in response to shrinking reserves, rather than simply adjusting excess reserves and Treasuries.

Large banks’ cash management strategies are heterogeneous and shift over time, indicating that changes in reserves demand are likely to be bank- and period-specific. For example, some banks substitute cash and Treasury balances one-for-one while others are less inclined to replace reserves with Treasury securities. Similarly, on the liabilities side, the desired cash levels of universal banks are strongly correlated with their anticipated retail deposit outflows, while institutional deposit outflows are more important for other banks. Finally, banks appear to have held larger cash balances in 2017 than 2016, even after accounting for changes in their balance sheets.

Interest Rates and Cash Balances

Lastly, large banks are sensitive to the opportunity cost of holding cash. For example, when Treasury bill rates increase relative to the Interest Rate on Excess Reserves (IOER), banks reduce the amount of cash they hold, all else equal. A few banks also hold less cash when the difference between repo rates and the IOER widens, making cash less attractive relative to secured assets.

The chart below plots indexes of the Treasury-IOER and repo-IOER spreads. Since 2017, these spreads--and the Treasury spread, in particular—have started to widen from a low base. This widening provides incentives for banks to substitute cash for other liquid, cash-like assets (and Treasuries, in particular). As rates rise, the cost of bank liabilities also increases and banks have a greater incentive to reallocate cash holdings into higher yielding liquid assets.

How Do Large Banks Manage Their Cash?

Summing Up

Why do large U.S. banks hold considerable amounts of low-yielding cash, other than to meet liquidity requirements? Our analysis shows that large banks have a long-run desired cash level and they manage their actual cash balances closely to their desired amounts. However, this desired amount varies over time with the composition of each bank’s balance sheet (which in turn is strongly influenced by its business model, more generally), and the opportunity cost of holding cash relative to earning assets.

Jeffrey Levine
Jeffrey Levine is a policy and market analysis senior associate in the Federal Reserve Bank of New York’s Markets Group.

Asani Sarkar
Asani Sarkar is an assistant vice president in the Bank’s Research and Statistics Group


How to cite this post:


Jeffrey Levine and Asani Sarkar, “How Do Large Bank Manage Their Cash?,” Federal Reserve Bank of New York Liberty Street Economics, July 17, 2019, https://libertystreeteconomics.newyorkfed.org/2019/07/how-do-large-banks....




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Large Bank Cash Balances and Liquidity Regulations

Published by Anonymous (not verified) on Mon, 15/07/2019 - 9:00pm in

Jeffrey Levine and Asani Sarkar

Update (9 a.m.): An earlier version of this post transposed line labels in the first figure. The error has been corrected.

First of two posts

Large Bank Cash Balances and Liquidity Regulations

The Federal Open Market Committee (FOMC) has recently communicated its aim to continue implementing monetary policy in a regime that maintains an ample supply of reserves, though with a significantly lower level of reserves than has prevailed in recent years. The liquidity needs of the largest U.S. commercial banks play an important role in understanding the banking system’s appetite for actual reserve holdings, which we refer to as bank reserve demand. In this post, we discuss the recent evolution of large bank cash balances and the effect of liquidity regulations on these balances. In part two of this series, we provide new evidence on how the largest banks manage their liquidity needs on a daily basis.

Why Do Large Banks’ Liquidity Needs Matter?

Before the financial crisis, there were few reserves and the Federal Reserve operated along the steeper portion of the demand curve for reserves (see the stylized figure below). By comparison, in the current monetary policy regime, there is an ample supply of reserves and banks’ demand is considered to be somewhere to the right of the steep portion of the demand curve, where the curve is flatter (point C in the figure below). In January 2019, the FOMC stated its intentions to continue to implement monetary policy in a regime with an ample supply of reserves. In determining the size of the Fed’s balance sheet (and thereby the level of reserves in the banking system) going forward, financial institutions’ demand for reserves is expected to play an important role.

Large Bank Cash Balances and Liquidity Regulations

If the supply of reserves falls much below current levels (to the left of C in the figure above), then banks might be willing to pay higher interest rates to acquire reserves, and the demand for reserves might become more sensitive to rates. How much supply needs to decline before this happens depends in part on how reserves are distributed among banks. In particular, if reserves are concentrated among large banks which tend to hold on to these reserves, then the supply of reserves needs to decline less before the demand for reserves becomes rate-sensitive. Thus, studying big banks’ liquidity management strategies helps us understand how the banks might react to changes in the supply of reserves and, thereby, how the distribution of reserves could change.

Since banks must hold some reserves due to reserve requirements, we focus on excess reserves—the amount that they hold over and above the required amount. Excess reserves are viewed by banks as cash equivalents, and so are particularly desirable for liquidity. In the chart below, we report bank holdings of excess reserves using the Board of Governors of the Federal Reserve System Consolidated Financial Statements of Bank Holding Companies Y-9C data of bank balance sheets. Although this variable does not precisely correspond to actual excess reserves holdings, we have verified (using confidential data) that the trends for it are similar to what we report. The eight largest globally systemically important U.S. banks (the so-called G-SIBs) on average held about 11 percent of their total assets in the form of excess reserves in 2009:Q1 but almost 14 percent in 2018:Q1 (blue line in chart below). By comparison, Treasury securities (which are also considered highly liquid assets) comprised 2 percent of bank assets in 2009:Q1 and 5 percent in 2018:Q1 (red line).

Large Bank Cash Balances and Liquidity Regulations

Liquidity Regulations and Large Banks’ Cash Balances

Recent liquidity regulations affect banks’ holding of excess reserves. The liquidity coverage ratio (LCR) mandates that banks hold enough high quality liquid assets (HQLA)—assets that can be liquidated within thirty days to cover banks’ net cash outflows during times of stress. Since excess reserves count towards banks’ HQLA requirements, banks subject to the LCR have an added incentive to hold reserves. Indeed, the share of excess reserves held by G-SIBs increased towards the end of 2012, just prior to the finalization of the LCR in 2013 (see blue line in chart above).

Using the LCR rules, we provide a rough estimate of HQLA from the Y-9C data. The estimate is approximate as there are nuances in the rules that we cannot accommodate with our data. We find that HQLA holdings as a share of G-SIB assets also started going up in anticipation of the LCR (see red line in chart below), and about half of HQLA requirements were satisfied with excess reserves (see blue line in chart below). By 2015, when banks began to implement LCR requirements, banks had adjusted their asset holdings, and the share of excess reserves and HQLA to total assets had stabilized.

Large Bank Cash Balances and Liquidity Regulations

Evolution of Large Bank Cash Balances as Aggregate Reserves Shrink

As the aggregate supply of reserves shrinks, large banks (which are subject to the LCR) may continue to hold some part of their HQLA target in reserves. Reserves are especially attractive as they can be used to meet obligations at any time without the need to sell or finance an asset in the market. Since the banking system as a whole must hold fewer reserves when the Fed’s balance sheet shrinks, this means that reserves could become even more concentrated at large banks. Alternatively, large banks might hold more Treasury or agency securities to offset the decline in the supply of reserves. These securities are also considered highly liquid, albeit not to the same degree as excess reserves. Finally, large banks might shrink their balance sheets by holding fewer liabilities, which could reduce their HQLA needs.

Banks’ desire to hold excess reserves relative to other HQLA, such as Treasuries or agency securities, depends in part on the differences in interest rates between them. In the chart below, we compare the three-month Treasury bill rate to the interest rate on excess reserves (IOER), the rate that the Fed pays on excess reserves. As the bill rate has decreased relative to the IOER (red line), bank holdings of excess reserves (blue line) have generally increased.

Large Bank Cash Balances and Liquidity Regulations

As large banks currently meet their HQLA targets in various ways, they may also manage their reserves differently, depending on their distinctive liquidity management strategies. In part two of this series, we examine large banks’ strategies in managing their liquidity on a daily basis.

Jeffrey Levine
Jeffrey Levine is a policy and market analysis senior associate in the Federal Reserve Bank of New York’s Markets Group.

Asani Sarkar
Asani Sarkar is an assistant vice president in the Bank’s Research and Statistics Group


How to cite this post:


Jeffrey Levine and Asani Sarkar, “Large Bank Cash Balances and Liquidity Regulations,” Federal Reserve Bank of New York Liberty Street Economics, July 15, 2019, https://libertystreeteconomics.newyorkfed.org/2019/07/large-bank-cash-ba....




Disclaimer


The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

MEDIA RELEASE: Alberta should increase social spending; cuts are not the way to go

(June 24, 2019-Calgary) With Alberta’s economy still facing challenges and vulnerabilities, the Alberta government should not be doling out tax cuts or cutting social spending, according to the Alberta Alternative Budget (AAB) released today.

“Alberta still has, by far, the lowest
debt-to-GDP ratio of any province,” says Nick Falvo, editor of the report. “We
are in a good position to increase spending on education, invest in affordable
child care, offer free dental care to Albertans under 18 years, and support
other programs that would help Albertans facing unpredictability in the job
market.”

The AAB is an annual exercise whose working
group consists of researchers, economists, and members of civil society. The
AAB  aims to create a progressive vision
for Alberta to boost economic growth and reduce income inequality.

Today’s report calls for the introduction
of a harmonized sales tax to reduce Alberta’s reliance on profit from energy
markets, that have always been volatile. Under the previous government,
important steps were taken to stabilize the economy through diversification and
social spending.

“The UCP government has already cut close
to $6 billion in provincial revenue by cancelling the carbon tax and cutting
corporate taxes, and this is the wrong direction,” says Falvo. “Instead,
investing in programs and infrastructure is what is needed to foster a vibrant
Alberta.”

Download the report.

-30-

Contact: Nick Falvo, falvo.nicholas@gmail.com, 587-892-7855

MEDIA RELEASE: Alberta should increase social spending; cuts are not the way to go

(June 24, 2019-Calgary) With Alberta’s economy still facing challenges and vulnerabilities, the Alberta government should not be doling out tax cuts or cutting social spending, according to the Alberta Alternative Budget (AAB) released today.

“Alberta still has, by far, the lowest
debt-to-GDP ratio of any province,” says Nick Falvo, editor of the report. “We
are in a good position to increase spending on education, invest in affordable
child care, offer free dental care to Albertans under 18 years, and support
other programs that would help Albertans facing unpredictability in the job
market.”

The AAB is an annual exercise whose working
group consists of researchers, economists, and members of civil society. The
AAB  aims to create a progressive vision
for Alberta to boost economic growth and reduce income inequality.

Today’s report calls for the introduction
of a harmonized sales tax to reduce Alberta’s reliance on profit from energy
markets, that have always been volatile. Under the previous government,
important steps were taken to stabilize the economy through diversification and
social spending.

“The UCP government has already cut close
to $6 billion in provincial revenue by cancelling the carbon tax and cutting
corporate taxes, and this is the wrong direction,” says Falvo. “Instead,
investing in programs and infrastructure is what is needed to foster a vibrant
Alberta.”

Download the report.

-30-

Contact: Nick Falvo, falvo.nicholas@gmail.com, 587-892-7855

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