Measuring Systemic Liquidity Risk and the Cost of Liquidity Insurance. By Tiago Severo

Measuring Systemic Liquidity Risk and the Cost of Liquidity Insurance. By Tiago Severo
IMF Working Paper No. 12/194
Jul 31, 2012
http://www.imfbookstore.org/ProdDetails.asp?ID=WPIEA2012194

Summary: I construct a systemic liquidity risk index (SLRI) from data on violations of arbitrage relationships across several asset classes between 2004 and 2010. Then I test whether the equity returns of 53 global banks were exposed to this liquidity risk factor. Results show that the level of bank returns is not directly affected by the SLRI, but their volatility increases when liquidity conditions deteriorate. I do not find a strong association between bank size and exposure to the SLRI - measured as the sensitivity of volatility to the index. Surprisingly, exposure to systemic liquidity risk is positively associated with the Net Stable Funding Ratio (NSFR). The link between equity volatility and the SLRI allows me to calculate the cost that would be borne by public authorities for providing liquidity support to the financial sector. I use this information to estimate a liquidity insurance premium that could be paid by individual banks in order to cover for that social cost.

Excerpts:

Introduction

Liquidity risk has become a central topic for investors, regulators and academics in the aftermath of the global financial crisis. The sharp decline of real estate prices in the U.S.  and parts of Europe and the consequent collapse in the values of related securities created widespread concerns about the solvency of banks and other financial intermediaries. The resulting increase in counterparty risk induced investors to shy away from risky short-term funding markets [Gorton and Metrick (2010)] and to store funds in safe and liquid assets, especially U.S. government debt. The dry-up in funding markets hit levered financial intermediaries involved in maturity and liquidity transformation hard [Brunnermeier (2009)], propagating the initial shock through global markets.

Central bankers in major countries responded to the contraction in liquidity by pumping an unprecendented amount of funds into securities and interbank markets, and by creating and extending liquidity backstop lines to rescue troubled financial intermediaries.  Such measures have exposed public finances, and ultimately taxpayers, to the risk of substantial losses. Understanding the origins of systemic liquidity risk in financial markets is, therefore, an invaluable tool for policymakers to reduce the chance of facing these very same challenges again in the future. In particular, if public support in periods of widespread distress cannot be prevented—due to commitment problems—supervisors and regulators should ensure that financial intermediaries are properly monitored and charged to reflect the contingent benefits they enjoy.

The present paper brings three contributions to the topic of systemic liquidity risk:

1) It produces a systemic liquidity risk index (SLRI) calculated from violations of “arbitrage” relationships in various securities markets.

2) It estimates the exposure of 53 global banks to this aggregate risk factor.

3) It uses the information in 2) to devise an insurance system where banks pre-pay for the costs faced by public authorities for providing contingent liquidity support.

Results indicate that systemic illiquidity became a widespread problem in the aftermath of Lehman’s bankruptcy, and it only recovered after several months. Systemic liquidity risk spiked again during the Greek sovereign crisis in the second quarter of 2010, albeit at much more moderate levels. Yet, the renewed concerns regarding sovereign default in peripheral Europe observed in the last quarter of 2010 did not induce global liquidity shortfalls.

In terms of exposures of individual institutions, I find that, in general, systemic liquidity risk does not affect the level of bank stock returns on a systematic fashion.  However, liquidity risk is strongly correlated with the volatility of bank stocks: system wide illiquidity is associated with riskier banks. Estimates also show that U.S. and U.K. banks were relatively more exposed to liquidity conditions compared to Japanese institutions, with continental European banks lying in the middle of the distribution. More specifically, the results indicate that U.S. and U.K. banks’ stocks became much more volatile relative to their Asian peers when liquidity evaporated. This likely reflects the higher degree of maturity transformation and the reliance on very short-term funding by Anglo-Saxon banks. A natural question is whether bank specific characteristics beyond geographic location reflect the different degrees of exposure to liquidity risk.

I start the quest for those bank characteristics by looking at the importance of bank size for liquidity risk exposure. Market participants, policymakers and academics have highlighted the role of size and interconnectedness as a source of systemic risk. To verify this claim, I form quintile portfolios of banks based on market capitalization and test whether there are significant differences in the sensitivity of their return volatility to the SLRI. The estimates suggest that size has implications for liquidity risk, but the relationship is highly non-linear. The association between size and sensitivity to liquidity conditions is only relevant for the very large banks, and it becomes pronounced only when liquidity conditions deteriorate substantially.

Recently, the Basel Committee on Banking Supervision produced, for the first time, a framework (based on balance sheet information) to regulate banks’ liquidity risk. In particular, it proposed two liquidity ratios that shall be monitored by supervisors: the Liquidity Coverage Ratio (LCR), which indicates banks’ ability to withstand a short-term liquidity crisis, and the Net Stable Funding Ratio (NSFR), which measures the long-term, structural funding mismatches in a bank. Forming quintile portfolios based on banks' NSFR, I find that, if anything, the regulatory ratio is positively associated with the exposure to the SLRI. In other words, banks with a high NSFR (the ones deamed to be structurally more liquid) are in fact slightly more sensitive to liquidity conditions. This counterintuitive result needs to be qualified. As noted later, the SLRI captures short-term liquidity stresses, whereas the NSFR is designed as a medium to long-term indicator of liquidity conditions. Certainly, it would be more appropriate to test the performance of LCR instead. However, the data necessary for its computation are not readily available.

The link between bank stock volatility and the SLRI allows me to calculate the cost faced by public authorities for providing liquidity support for banks. Relying on the contingent claims approach (CCA), I use observable information on a bank’s equity and the book value of its liabilities to back out the unobserved level and volatility of its assets. I then estimate by how much the level and volatility of implied assets change as liquidity conditions deteriorate, and how such changes affect the price of a hypothetical put option on the bank’s assets. Because the price of this put indicates the cost of public support to banks, variations in the put due to fluctuations in the SLRI provide a benchmark for charging banks according to their exposure to systemic liquidity risk, a goal that has been advocated by many experts on bank regulation. 


http://www.imf.org/external/pubs/cat/longres.aspx?sk=26131.0

Austerity Debate a Matter of Degree -- In Europe, Opinions Differ on Depth, Timing of Cuts; International Monetary Fund Has Change of Heart

Austerity Debate a Matter of Degree. By Stephen Fidler
In Europe, Opinions Differ on Depth, Timing of Cuts; International Monetary Fund Has Change of Heart
Wall Street Journal, February 17, 2012
http://online.wsj.com/article/SB10001424052970204792404577227273553955752.html

Excerpts

In the U.S., the debate about whether the government should start cutting its budget deficit opens up a deep ideological divide. Many countries in Europe don't have that luxury.

True, there may be questions about how hard to cut budgets and how best to time the cuts, but with government-bond investors going on strike, policy makers either don't have a choice or feel they don't. Budget austerity is also a recipe favored by Germany and other euro-zone governments that hold the Continent's purse strings.

Once upon a time, the International Monetary Fund, which also provides bailout funds and lend its crisis management expertise to euro-zone governments, would have been right there with the Germans: It never handled a financial crisis for which tough austerity wasn't the prescribed medicine. In Greece, however, officials say the IMF supported spreading the budget pain over a number of years rather than concentrating it at the front end.

That is partly because overpromising the undeliverable hurts government credibility, which is essential to overcoming the crisis. But it is also because the IMF's view has shifted.

"Over its history, the IMF has become less dogmatic about fiscal austerity being always the right response to a crisis," said Laurence Ball, economics professor at Johns Hopkins University, and a part-time consultant to the IMF.

These days, the fund worries more than it did about the negative impact that cutting budgets has on short-term growth prospects—a traditional concern of Keynesian economists.

"Fiscal consolidation typically has a contractionary effect on output. A fiscal consolidation equal to 1% of [gross domestic product] typically reduces GDP by about 0.5% within two years and raises the unemployment rate by about 0.3 percentage point," the IMF said in its 2010 World Economic Outlook:

But that isn't the full story. In the first place, the IMF agrees that reducing government debt—which is what austerity should eventually achieve—has long-term economic benefits. For example, in a growing economy close with strong employment, reduced competition for savings should lower the cost of capital for private entrepreneurs.

That suggests that, where bond markets give governments the choice, there is a legitimate debate to be had about timing of austerity. The IMF economic models suggest it will be five years before the "break-even" point when the benefits to growth of cutting debt start to exceed the "Keynesian" effects of austerity.

There is an alternative hypothesis that has a lot of support in Germany, and among the region's central bankers. This is the notion that budget cutbacks stimulate growth in the short term, often referred to as the "expansionary fiscal contraction" hypothesis.

Manfred Neumann, professor emeritus of economics at the Institute for Economic Policy at the University of Bonn, said the view is also called the "German hypothesis" since it emerged from a round of German budget cutting in the early 1980s.

"The positive effect of austerity is much stronger than most people believe," he said. The explanation for the beneficial impact is that cutting government debt generates an improvement in confidence among households and entrepreneurs, he said.

The IMF concedes there may be something in this for countries where people are worried about the risk that the government might default—but only up to a point. It concedes that fiscal retrenchment in such countries "tends to be less contractionary" than in countries not facing market pressures—but doesn't conclude that budget cutting in such circumstances is actually expansionary.

Each side of the debate invokes its own favored study. Support for the "German hypothesis" comes from two Harvard economists with un-German names—Alberto Alesina and Silvia Ardagna. But their critics, who include Mr. Ball, say their sample includes many irrelevant episodes for which their model fails to correct—including, for example, the U.S. "fiscal correction" that was born out of the U.S. economic boom of the late 1990s.

Mr. Alesina didn't respond to an email asking for comment, but Mr. Neumann said he isn't confident that studies, such as the IMF's, that appear to refute the hypothesis manage to isolate the effects of the austerity policy from other effects of a financial crisis.

Some of the IMF's conclusions, however, bode ill for the euro zone's budget cutters.

The first is that the contractionary effects of fiscal retrenchment are often partly offset by an increase in exports—but less so in countries where the exchange rate is fixed. Second, the pain is greater if central banks can't offset the fiscal austerity through a stimulus in monetary policy. With interest rates close to zero in the euro zone, such a stimulus is hard to achieve. Third, when many countries are cutting budgets at the same time, the effect on economic activity in each is magnified.

If you are a government in budget-cutting mode, there are, however, better and worse ways of doing it. The IMF says spending cuts tend to have less negative impact on the economy than tax increases. However, that is partly because central banks tend to cut interest rates more aggressively when they see spending cuts.

Mr. Neumann sees an austerity hierarchy. It is better to cut government consumption and transfers, including staff costs, than government investment—though it may be harder politically. If you are raising taxes, better to raise those with no impact on incentives—such as inheritance or wealth taxes—than those that hurt incentives, such as income or payroll taxes.

Raising sales or value-added taxes may have less impact on incentives—but have other undesirable effects, such as increasing inflation, that could deter central banks from easing policy.

The Statistical Definition of Public Sector Debt. An Overview of the Coverage of Public Sector Debt for 61 Countries

What Lies Beneath: The Statistical Definition of Public Sector Debt. An Overview of the Coverage of Public Sector Debt for 61 Countries.By Robert Dippelsman, Claudia Dziobek, and Carlos A. Gutiérrez Mangas
IMF Staff Discussion Note
http://www.imf.org/external/pubs/cat/longres.aspx?sk=26101.0

Excerpts

Executive Summary

While key macroeconomic indicators such as Gross Domestic Product (GDP) or Consumer Price Index (CPI) are based on internationally accepted methodologies, indicators related to the debt of the public sector often do not follow international standards and can have several different definitions. As this paper shows, the absence of the standard nomenclature can lead to major misunderstandings in the fiscal policy debate. The authors present examples that show that debt-to-GDP ratios for a country at any given time can range from 40 to over 100 percent depending on the definition used. Debt statistics, for example, may include or exclude state and local governments and may cover all debt instruments or just a subset. The authors suggest that gross debt of the general government (―gross debt‖) should be globally adopted as the headline indicator supplemented by other measures of government debt for risk-based assessments of the fiscal position. Broader measures, including net debt and detailed information on contingent liabilities and derivatives, could be considered. The standard nomenclature of government and of debt instruments helps users understand the concepts in line with the Public Sector Debt Statistics Guide. Use of more standard definitions of government debt would improve data comparability, would benefit IMF surveillance, programs, and debt sustainability analysis, and would help country authorities specify and monitor fiscal rules. Data disaggregated by government subsector and debt instrument for 61 countries from the IMF‘s Government Finance Statistics Yearbook (GFSY) database are presented to illustrate the importance and viability of adopting this approach.

Most key macroeconomic indicators such as GDP, the consumer price index (CPI), data on monetary aggregates or balance of payments follow internationally accepted definitions. In contrast, countries often do not follow international guidelines for public debt data. As this paper shows, failure to apply global standards can lead to important misunderstandings because of the potentially large magnitudes involved. International guidelines on the compilation of public sector debt are well established and are summarized in the recently published Public Sector Debt Statistics Guide (Debt Guide). The Debt Guide also describes applications of these guidelines for the analysis of debt sustainability, fiscal risk, and vulnerability.

The authors seek in this paper to provide a more intuitive application of the various concepts and definitions found in the Debt Guide, and propose that global standard definitions of ―gross debt‖ referring to the ―general government‖ be adopted as a headline measure. As with other headline indicators, a variety of narrower and wider indicators remain valuable and useful for different purposes. The notion of gross debt will be familiar to macroeconomic statisticians, but, as a practical matter, the adoption of global standard statistical definitions of debt will require some development efforts in terms of source data availability and training for compilers of debt statistics. A particular challenge is complete coverage of all relevant institutions and financial instruments. Detailed information on contingent liabilities and derivatives should also be considered. Coordination across agencies that work with debt related data is also critical, as with other complex datasets such as GDP.

Many users are not aware of the extent to which differences in concepts and methods matter. Box 1 below highlights the four key dimensions of public sector debt. Countries publish data, for example, either including or excluding state and local governments, pension funds, and public corporations. Also, while much of the policy debate centers on government liabilities, some countries have begun to publish and focus policy analysis on net debt (financial assets minus liabilities). Debt data frequently only include two (of the six) debt instruments available: debt securities and loans. Debt instruments such as other accounts payable or insurance technical reserves are often not taken into account. In many cases the method of valuation is not explicitly mentioned even though market versus nominal valuation can be significantly different. Consolidation, which refers to the process of netting out intra-governmental obligations, is another important factor rarely specified in published data. And finally, debt data may be compiled using cash data and excluding non-cash items such as arrears or using accrual (or partial accrual) methods to reflect important non-cash obligations.

Box 1. Key Dimensions to Measure Government Gross Debt
Institutional Coverage of Government
Instrument Coverage of Debt
Valuation of Debt Instruments (market and nominal)
Consolidation of Intra-Government Holdings

Source: Public Sector Debt Statistics Guide.

Conclusions

The headline indicator for government debt should be defined as ―gross debt of the general government‖ or GL3/D4 in this paper‘s nomenclature. The authors suggest that countries should aspire to publish timely data on the broader concept of gross debt.

Data on the institutional level of the general government (GL3) would be consistent with a broad range of data uses and with the data requirements of other macroeconomic datasets, notably the national accounts. Including the full range of debt instruments is desirable particularly because some of these may expand in times of financial distress and could thus serve as valuable indicators of distress. Clarity of what the debt data cover would help build understanding of the data and their comparability across countries.

A global standard would facilitate communication on the main concepts in public sector debt statistics and it would bring greater precision to research on fiscal issues, and lead to improved cross-country comparison. This framework uses a nomenclature inspired by the approach in monetary data where M1 through M4 (monetary aggregates) reflect institutional and instrument coverage as well.

The methodological framework of government debt presented here is widely accepted among statisticians. The relevant definitions, concepts, classification, and guidance of compilation are summarized in GFSM 2001 and the Debt Guide. These standards are fully consistent with the overarching statistical methodology of the 2008 SNA and other international macroeconomic methodologies such as the Sixth Edition of Balance of Payments and International Investment Position Manual (BPM6) and broadly consistent with the European System of Accounts (ESA) manual and the more specialized manuals of deficit and debt that govern the Excessive Deficit Procedure.
However, the methodology is not always well defined in the policy debate. An international convention to view GL3/D4 as the desirable headline indicator of government debt, consistent with the international standards, would go a long way to create more transparency and better comparability of international data.

Our contribution is to provide a presentational framework and nomenclature that highlights the importance of different instruments, institutional coverage, and valuation and consolidation as key indicators of debt. Indeed, we have noted that other, more narrowly defined concepts can meaningfully supplement the comprehensive measure of debt. These narrower measures may be important for a risk-based assessment of the fiscal position, but they are not substitutes for a global indicator.

Further extensions of this work are the development of the statistical reporting of broader measures, for example net debt of the general government and the presentation of information on derivatives, and contingent liabilities.

The new debt database launched by the IMF and World Bank in 2010 is structured along government levels, debt instruments, consolidation and valuation as discussed in this paper. However, some countries report data only on the GL2 level and cover mostly D1. Developing data on the broader statistics will take some time, although Australia, Canada, and some other countries already publish or plan to publish GL3/D4 data or publish components that would allow the calculation of GL3/D4.

Debt statistics for various levels of government and instruments were shown for 61 countries and these data highlight some interesting patterns that merit further analysis such as the degree of fiscal autonomy of state and local government to issue debt, the degree of development of markets for government debt securities. The authors conclude that further research would be worthwhile on the advantages of a global standard of government debt for such topics as data comparability, IMF surveillance, programs, debt sustainability analysis, and the analysis of fiscal rules.

BIS - Capital requirements for bank exposures to central counterparties + Basel III counterparty credit risk FAQs + other doc

Basel III counterparty credit risk - Frequently asked questions (update of FAQs published in November 2011) (25.07.2012 12:10)
http://www.bis.org/publ/bcbs228.htm

Regulatory treatment of valuation adjustments to derivative liabilities: final rule issued by the Basel Committee (25.07.2012 12:05)
http://www.bis.org/press/p120725b.htm

Capital requirements for bank exposures to central counterparties (25.07.2012 12:00)
http://www.bis.org/publ/bcbs227.htm

On Graen's "Unwritten Rules for Your Career: 15 Secrets for Fast-track Success"

Miner (2005) says (chp 14), citing Graen (1989), that those interested in achieving their personal ends would need to focus on:

things a person should do to achieve fast-track status in management, what unwritten rules exist in organizations, and how to become an insider who understands these rules and follows them to move up the hierarchy. These unwritten rules are part of the informal organization and constitute the secrets of organizational politics.


There are fifteen such secrets of the fast track:

1. Find the hidden strategies of your organization and use them to achieve your objectives.  (This involves forming working relationships—networks—with people who have access to special resources, skills, and abilities to do important work.)

2. Do your homework in order to pass the tests. (These tests can range from sample questions to command performances; you should test others, as well, to evaluate sources of information.)

3. Accept calculated risks by using appropriate contingency plans. (Thus, learn to improve your decision average by taking calculated career risks.)

4. Recognize that apparently complete and final plans are merely flexible guidelines to the actions necessary for implementation. (Thus, make your plans broad and open-ended so that you can adapt them as they are implemented.)

5. Expect to be financially undercompensated for the first half of your career and to be overcompensated for the second half. (People on the fast track inevitably grow out of their job descriptions and take on extra duties beyond what they are paid to do.)

6. Work to make your boss successful. (This is at the heart of the exchange between the two of you and involves a process of reciprocal promotion.)

7. Work to get your boss to promote your career. (This is the other side of the coin and involves grooming your replacement as well.)

8. Use reciprocal relationships to build supportive networks. (It is important that these be competence networks involving effective working relationships and competent people.)

9. Do not let your areas of competence become too narrowly specialized. (Avoid the specialists trap by continually taking on new challenges.)

10. Try to act with foresight more often than with hindsight. (Be proactive by identifying the right potential problem, choosing the right solution, and choosing the best implementation process.)

11. Develop cordial relationships with your competitors: Be courteous, considerate, and polite in all relationships. (You need not like all these people, but making unnecessary enemies is an expensive luxury.)

12. Seek out key expert insiders and learn from them. (Have numerous mentors and preserve these relationships of your reciprocal network.)

13. Make sure to acknowledge everyone’s contribution. (Giving credit can be used as a tool to develop a network of working relationships.)

14. Prefer equivalent exchanges between peers instead of rewards and punishments between unequal partners. (Equivalent exchanges are those in which a resource, service, or behavior is given with the understanding that something of equivalent value will eventually be returned; this requires mutual trust.)

15. Never take unfair advantage of anyone, and avoid letting anyone take unfair advantage of you. (Networks cannot be maintained without a reputation for trustworthiness.)


More recently, in another book, Graen (2003) has revisited this topic and set forth another partially overlapping list of thirteen actions that distinguish key players from others [...]. These guidelines [...] for how to play the hierarchy and gain fast-track status are as follows:

1. Demonstrate initiative to get things done (i.e., engage in organizational citizenship behaviors).

2. Exercise leadership to make the unit more effective (i.e., become an informal group leader).

3. Show a willingness to take risks to accomplish assignments (i.e., go against group pressures in order to surface problems if necessary).

4. Strive to add value to the assignments (i.e., enrich your own job by making it more challenging and meaningful).

5. Actively seek out new job assignments for self-improvement (i.e., seek out opportunities for growth).

6. Persist on a valuable project after others give up (and learn not to make the same mistake twice).

7. Build networks to extend capability, especially among those responsible for getting work done.

8. Influence others by doing something extra (i.e., this means building credibility and adjusting your interpersonal style to match others).

9. Resolve ambiguity by dealing constructively to resolve ambiguity (i.e., gather as much information as possible and obtain frequent feedback).

10. Seek wider exposure to managers outside the home division, which helps in gathering information.

11. Build on existing skills. Apply technical training on the job and build on that training to develop broader expertise; be sure not to allow obsolescence to creep in.

12. Develop a good working relationship with your boss. Work to build and maintain a close working relationship with the immediate supervisor (Strive to build a high quality LMX, devote energy to this goal—see Maslyn and Uhl-Bien, 2001).

13. Promote your boss. Work to get the immediate supervisor promoted (i.e., try to make that person look good; as your boss goes up, so well may you).

Bibliography
Graen, George (1989). Unwritten Rules for Your Career: 15 Secrets for Fast-track Success. New York: John Wiley.
Graen, George (2003). Dealing with Diversity. Greenwich, CT: Information Age Publishing.
Miner, John B. Organizational behavior I. Essential theories of motivation and leadership. Armonk, NY: M. E. Sharpe.