This crisis is not about Greece, in same way as the 2007 crisis was not about subprime. Instead, we should be thankful to the Greeks for being so irresponsible.

The sooner the better we can focus on the real problem the better we are, so thank you Greece for focusing our minds. After all, the costs of a Greek default will be couple of percentage points of eurozone GDP, easily manageable

On a general level, we could say this is a systemic crisis in making with 2 sub crisis

1. a banking crisis or a confidence crisis or liquidity crisis
2. Sovereign debt

Let us talk about the banks first. They do have considerable exposure to Greece, and let us be clear about this a key reason is government. In their wisdom, Greek debt is considered safe when it comes to capital while lending to AAA rated Microsoft attracts a significant capital charge. This in spite of the fact that Greek debt was A before the crisis started.

Fortunately, banks have been quite active in writing down Greek debt, even though some are more vulnerable than others. In a way, the crisis demonstrates which banks are weakest. In this, it is interesting to note when the European authorities run stress tests on European banks they assume no sovereign can default.

The second crisis is a sovereign debt crisis. Again, if it was only about Greece it would not be a problem. Even though we can question the morality of having poor well-run countries like Slovenia subsidize much richer Greece. The elephant in the room is Italy. It also is irresponsible but is much bigger. What the European authorities are debating is not the Greek bailout but really the Italian bailout

Each sub crisis is serious but not in any way systemic. What makes it systemic are European politics. They have 2 choices. They can decide to bailout or default. You may have a preference for either of those options but regardless, either will solve the crisis.

Instead, they opted to muddle through. Every time Greece needs another 5 billion, every euro zone country has to agree, a big political fight ensues, newspaper headlines spell doom. It is this uncertainty is creating a systemic crisis.

If Europe will suffer a systemic crisis it is because of its dysfunctional political structure and the shortsightedness of its national leaders. Indeed, if we look at history, none of the really big crisis are created by the financial system. Instead they were all created by politicians. And therefore, inappropriate policy response has been the main cause of all the world’s biggest financial crisis. With that in mind, how can we really expect the politicians to regulate the financial system.

The IMF staff have an inflated view of their abilities and contribution to Iceland’s recovery along with an ironclad conviction that they are right. Which they are not, as I discuss here.

They seem to have embraced Krugman’s liking of “unorthodox economics” view in their writeup: Iceland’s Unorthodox Policies Suggest Alternative Way Out of Crisis. Its worth noting that the IMF insisted on very orthodox measures, like extreme interest rates, hawking back to their 1997 Asia crisis advice.

Finally, none of the foreign stars in the conference let an almost complete lack of knowledge about Iceland prevent them from making detailed comments about the economic situation in Iceland. Pathetic really.

Iceland has just become the first industrialised country to graduate from an IMF programme in over 30 years. The IMF claims the programme has been an unqualified success:

Iceland has successfully completed its Fund-supported programme. Key objectives have been met: public finances are on a sustainable path, the exchange rate has stabilised, and the financial sector has been restructured. Strong policy implementation has underpinned this success (IMF 2011).

This was the first time the Fund had mounted a rescue operation for one of the richest and most developed countries in the world, closely interlinked with the northern European economies. With the Fund’s expertise honed on crises in less-developed countries, there have been doubts about its ability to successfully execute the Icelandic programme.

The facts don’t match the claims

Based on the current state of the Icelandic economy, the Fund’s claim of success does not stand up to scrutiny.

• Public finances are not on a sustainable path,
• Exchange rates are not fully stable even with capital controls,
• Investment has collapsed, and
• The financial sector is dysfunctional.

At the same time, the Fund forced Iceland to impose a high interest-rate policy at the time when every other developed economy was doing the opposite.

• Iceland is in a recession.¹

GDP has declined by about 11% since the crisis of October 2008, but modest and volatile growth has returned, sustained primarily by an increase in private consumption catching up after two years of austerity. Worryingly, export growth is low, even with a sharp fall in the exchange rate, while investment is at a record low.

Business investment rates in Iceland equalled the EU average from 1995 to 2008, according to Eurostat.

• Over the past two years the investment rate in Iceland collapsed to 10% whilst the EU only suffered a small decline to 17%.

This means Iceland had the second lowest investment rate in Europe in 2010, after Ireland. That is not surprising when, according to the OECD, Iceland had the highest number of restrictions on foreign direct investment among member countries in 2010 (Kalinova et al 2010).

• Government debt, central and local, equals annual GDP and is rising.

The government enjoys a primary surplus if we exclude one-off items, but considering the annual flow of revenue and expenditures, it runs a primary deficit. Government revenue and expenditure increased sharply in the bubble years and the government has had difficulties in adjusting to the revenue shock after the crisis. It raised taxes and reduced expenditures, but in order to get it is financing on a sustainable level it needs to do more, but is hindered by a strong opposition to its fiscal policies. Firm IMF encouragement for taking the necessary measures towards sustainable public finances would have been useful.

Still, the authorities have significantly reduced the risk of a sovereign default, and chalked up a notable success when Iceland was able to tap the international capital markets earlier this year, borrowing $1 billion at 6%.

Requiring orthodox monetary policy

Most economies faced a negative shock at the same time as Iceland and the reaction of their monetary authorities was generally monetary expansion through low interest rates and quantitative easing. Immediately following the collapse, the Icelandic central bank followed, but soon was forced to increase interest rates to 18% “as a condition of a proposed $2 billion loan from the International Monetary Fund” according to the Financial Times (see Ibison 2008). The IMF allowed Iceland to reduce its central bank interest rates in March 2009 and they remained in double digits until 2010. As inflation was 7% during that period, while the economy was sharply contracting, it is hard to see what benefit the Fund saw in implementing such orthodox monetary policy.

In this, the IMF seems to have been following similar conditions it imposed on the Southeast Asian countries in their 1997 crisis, subsequently widely criticised as significantly contributing to that crisis.

Since the start of the crisis, inflation has been 6% on average, now at 5%, and central bank interest rates have been steadily falling, to 4.5% now. The Icelandic monetary policy is at odds with that of other developed countries. For example, inflation in the UK exceeds Icelandic rates at 5.2% but its interest rates are below 1%. The situation is similar in the Eurozone and the US.

Interestingly, the high interest rates were often justified by reference to the need to stabilise the exchange rate, but as the IMF also insisted on capital controls, in the subsequent closed economy, low interest rates could have been used to stimulate the economy without any risk to the exchange rate.

The IMF (2011) finds that “The tightening bias in monetary policy is appropriate”. This assessment does not seem based on the available facts nor economic logic. The Fund’s demand for high interest rates was unjustified, causing significant economic damage.

Capital controls

Before its crisis, the amount of foreign hot money flowing into Iceland was close to 50% of GDP, with the government at the time highly supportive of the inflow. Anticipating the crisis, the currency market effectively closed in September 2008, with both foreign carry traders and domestic investors seeking to leave, but with few takers of the Icelandic Krona the exchange rate depreciated sharply.

After the crisis, the remaining carry traders, along with many local investors, wanted to leave, which likely would have caused a sharp short term drop in the exchange rate. As a consequence, the Icelandic government, with the strong encouragement of the IMF, imposed stringent capital controls, not only preventing the carry traders from exiting, but also requiring rarely granted government approval for Icelanders to invest abroad.

Iceland was the last OECD member country to abolish capital controls in 1993 and the first to adopt them again. While capital controls are often considered a useful tool to prevent hot money inflows, the objective and especially the implementation of the Icelandic capital controls has been different than usual current practice, they are more akin to the overarching capital controls of the 1950s than the more surgical form more common recently.

The central bank runs a strict licencing regime, rationing access to foreign currency. The immediate consequence has been the emergence of a dual exchange-rate regime, with offshore rates often 30%-40% higher than domestic rates. In turn, the government has been playing cat and mouse with currency traders, generally tightening regulations as market participants find new loopholes.

Initially, the capital controls were touted as a temporary measure to prevent a sharp depreciation of the currency, but by now the domestic economy has adapted to their presence, and become increasingly inward looking. The signs point to the controls remaining.

The capital controls have led to a form of exchange-rate stability, with the annualised volatility of the euro-krona exchange rate over the past year at 10%. By contrast, the euro-Swedish krona volatility has been 7% and the euro-dollar volatility 11%. With the presence of tightly binding capital controls, we might reasonably have expected lower exchange-rate volatility with the main trading partner.

Political risk and FDI

As noted by the OECD (2010), the Icelandic authorities have traditionally not been welcoming to potential investors, especially in its fishing sector. This anti-investment bias has worsened after the crisis with rules and conditions changing frequently. The government has been two-faced in its treatment of potential foreign investors, and its duplicity has injected fear and mistrust into the investment environment.

Unfortunately, the government has also been using the capital controls as means to implement industrial policy, politically selecting those allowed to use cheap offshore kronas to buy Icelandic assets. Such direct political selection of investors can only breed corruption, mistrust, and inefficiency.

Banking system

I discussed the banking system in an article on this site yesterday (Danielsson 2011), so to summarise, the Icelandic authorities were faced with the collapse of its entire banking system in October 2008. Instead of following best practice, splitting the failing banks into good banks and bad banks, they opted to split the banks on national lines, with the result that Iceland was left with three new banks that are unable to provide normal banking services. Subsequently, two of those banks have mostly passed into the hands of unknown foreign vulture funds, whose objective is to maximise asset recovery instead of banking.

As a consequence, Iceland has a dysfunctional banking system acting as a serious drag on economic recovery.

Conclusion

Iceland just ended the IMF programme it has been in since its crisis at the end of 2008. It is hard to see much success. The macroeconomic situation is dire, the banking system is dysfunctional, private investment has collapsed, and capital controls are here to stay. To their credit, the authorities have been successful in averting a sovereign default and regaining access to international capital markets.

Of course, we cannot blame the IMF for all of this. After all, the Icelandic government is ultimately responsible for implementing policy, even if the Fund did exercise direct authority by insisting on policy measures such as sharply raising interest rates during the worst of the crisis.

Overall, the Fund’s unqualified claim of success does not seem to be warranted.

However, little transparency exists on the IMF’s role in Iceland, and we do not know whether the Fund actively supported or even directed policy, or simply stood by while the Icelandic authorities took action. At the same time, it is unclear whether the IMF is still providing advice.

The Icelandic banking system collapsed in October 2008 and its three internationally active banks were taken over by the government. Disregarding best international practice, the government opted to restructure the banks on national grounds. Soon after two of the three passed into the hands of foreign ‘vulture’ funds which had no expertise or interest in running a banking system – they just wanted to cash out assets. The government has also maintained direct control of the banks, implementing an overarching regulatory structure and discouraging regular banking activities.

As a result, Iceland is saddled with a dysfunctional banking system more interested in maximising short-term asset recovery and compliance than providing the core banking services necessary for economic recovery.

Crisis and resolution

Best practice in resolving banking crises is the good bank/bad bank model, as used by Sweden in 1992 (Buiter 2009, Jonung 2009). A key objective is to create viable financial institutions able to provide uninterrupted banking services, thus helping economic recovery. In spite of receiving advice from Scandinavia, the Icelandic authorities at the time of the collapse rejected this approach.

Instead, they opted to split the banks on geographical lines. Keep the Icelandic operations separate from the international operations, with the idea that somehow Iceland could be protected from international default. Not surprisingly, this was impossible. Not only were the Icelandic assets of low quality and part of the same asset pool as foreign assets, but international creditors had claims on both types of assets so bank resolution schemes necessarily had to consider both types jointly.

The end result was three new domestic banks, not quite stillborn, but as they were saddled with the domestic loan book of the old banks, they were anything but healthy.

The authorities at the time had it within their powers to do a good bank/bad bank split, but within a few months that option disappeared as the resolution process moved into its second phase.

A new banking system is formed

Soon after the banks collapsed, their international creditors wrote down their Icelandic exposures, selling them on the secondary market for around 4-6 cents on the dollar. The purchasers were mostly vulture funds specialising in distressed assets.

After a change in government in early 2009, it fell on the incoming government, along with the IMF, to reconcile creditor demands. The international parts of the banks were left in the winding up stage with large international banks remaining significant creditors, while the domestic parts, called new banks, took over retail banking responsibilities in Iceland and domestic asset recovery.

Two of the three new banks passed indirectly into the hands of foreign vulture funds in 2009, with the government retaining a share and a veto power on the board. The last bank, Landsbanki, is likely to remain in government hands – at least until the Icesave issue is settled.

The new beneficial owners of the new banks are vulture funds that have neither much banking experience nor any interest in running a banking system. Instead they specialise in maximising short-term distressed asset recovery. However, their freedom to act is limited by both the legal resolution process and the government, at least until they formally assume ownership when the new banks become incorporated.

Regulations and governance

After the crisis of October 2008, the government was determined to prevent a future crisis and in short order created an expansive regulatory structure suitable for the three international Icelandic banks prior to the collapse, a regulatory structure that supposedly would have prevented the banking crisis ex post. This approach failed to take into account that the banks emerging after the collapse were quite different than before, only a very small fraction of their previous size, and focused on domestic operations.

After the crisis, banks and bankers were demonised in the media and the incoming bank administrators, chosen because they were not bankers, adopted that view, beefing up compliance and sharply curtailing regular banking operations.

One example of the regulatory changes was an immediate increase in capital requirements to 16%. In light of the current international debate on the impact of increasing bank capital to much lower levels and the long timetable for the implementation, it is hard to see the logic behind that decision.

The banks as asset managers

The new beneficial owners of the banks treat them more as asset management institutions than providers of financial services. That entails maximising recovery from creditors and profiting from running failed firms that they have fully or partially taken over. The banks are reluctant to write down loans, so they roll them over. The practice, known as ‘evergreening’, is more the norm than the exception.¹

Firms that escaped default in the crisis are at a significant disadvantage compared to worse-run competitors who failed and passed into the hands of the banks since the banks favour their own companies in access to credit. In turn, this holds back recovery and discourages investment because any new firm would have to obtain banking services from the very same banks competing against them.

Conclusion

If the banking system of Iceland had been restructured on best practice international grounds, Iceland would now have a vibrant banking system, providing the necessary if not sufficient conditions for economic recovery. Instead, the Icelandic government, with IMF support, created a dysfunctional banking system that continues to prevent recovery.

The government can still solve the problem, starting by clarifying beneficial ownership of the banks, reducing its focus on compliance and regulations, and encouraging normal bank practices. At the same it should require the direct separation between the asset management function of the banks and the provision of banking services.

I do however question the qualifications of those calling for and making new regulations. After all, the latest crisis is wholly created by governments themselves and even worse, they keep on making the crisis worse by inability to make decisions. This does not show a good example for how to make financial decisions.

How can the very same governments and their financial experts be qualified to design effective future financial regulations if they display such incompetence in managing their own financial affairs?

I think all of this suggests what asset class one should be investing in.

European politicians will make decisions in the next few weeks that are likely to determine the future prosperity of Europe. The leaders have two choices. Feed the crisis by ignorance, political disagreement, forbearance, muddling through and inaction, or recognize reality, face the upfront cost of restructuring, remove the information uncertainty and create conditions for future prosperity. By our analysis the relative benefit of opting a properly managed default is a gain of 22% of the European GDP over the coming two decades.

In order to estimate the cost of the ostrich attitude it is useful to look at what has happened in other large economies facing similar choices, with no better example but Japan, as the EU approach to crisis management does have a strong parallel with how Japan handled its crisis in the early 1990s. The slow crisis management approach of the Japanese state to its crisis is eerily similar to what we see now from the EU authorities: Hoping for the best, doling out partial bailouts, ever greening loans, and trying to maintain the status quo ex ante. This created zombie banks, triggering years of debt deflation and economic stagnation. The cost to Japan has been an economic growth of 0.67% a year for the past two decades, a reasonable estimate if the EU opts for inaction.

In the case of the Euro, the main reason for slow growth would be the continuing political risk and the ongoing uncertainty regarding the strength and exposures of commercial banks. There is even a real fear we have started to see the emergence of zombie banks Japanese style, as the ECB is becoming the lender of first resort for many banks, entire banking systems and even national governments. This leads to a vicious feedback loop between the dry-up of liquidity, reduced lending and diminished economic growth.

The alternative for the EU authorities is to recognize reality and allowing restructuring ofGreek and possibly Portuguese debt.

We suspect the benefits of a default would be substantial for the entire European economy, including those defaulting. A default will break the vicious cycle between a lack of confidence, banking crisis, increasing borrowing costs and fall in liquidity has been created.

A reasonable estimate of the cost of recognizing realty is the average growth in the Eurozone over the past 20 years or 1.67%. In this case the euro area GDP would be 22% higher after 20 years, or €3.5 trillion, while muddling only yields €1.3 trillion in GDP.

It would be preferable if the default were orderly instead of being the chaotic endgame it would likely be in the ostrich approach. The authorities would need to be prepared for possible liquidity and balance of payments problems for countries like Italy and Spain. The IMF, the institution set up for that purpose, should stand ready to provide the necessary support.

The financial system in many countries is in effect an oligopoly of very large powerful and well—connected financial institutions. In some countries, the industry might have very close connections with the government, such as France, while in other countries the relationship is more arms length. Indeed, one could say the more banks a country has, the less political power they have. The revolving door between the industry and government in some countries, like the US, can exasperate this problem.

Government institutions are also at a disadvantage when it comes to the industry. Not only can banks vastly outbid the government when it comes to human capital, they also have orders of magnitudes more people working for them. This can be particularly problematic when it comes to addressing highly complex issues by relatively junior staff members.

In addition, the governments problem in understanding the complexity of the financial system is made worse by the fact that it is task is more difficult the task of any bank. The bank has only to worry about its own risk, the government has to worry about the risk of each and every bank, individually and in aggregate — an impossible task.

All of this suggests that when it comes to regulating the financial industry and providing bailouts when it fails, the interests of the taxpayers are at a serious disadvantage. Not only can the banks exert targeted lobbying at the government, the technical ability of the government to respond effectively unlimited.

In a crisis, this becomes especially difficult. Senior government ministers and their advisers are unlikely to understand the underlying problem in detail. How is the government to know when a banker comes to it saying “if you do not bail me out the financial system will  collapse” whether that is true or not? If the government then brings in expertise from the industry, perhaps even putting them in charge of government ministries, it is unclear whom they actually represent. The interests of industry or the interests of the taxpayer.

Chomsky is one of the most perceptive public intellectuals around and listening to him is always a delight. However, in this interview he strayed into the areas of my expertise — economics and Iceland and either told things that are not true, misrepresented facts or did not provide the necessary connection to understand what he was advocating. There were several such things in his discourse, here are the three worst:

1. He said Iceland had found a unique solution, including not paying back the money. He was not clear on who paid the money back, but we do know that the government of Iceland has paid back every penny it owes and been rewarded with lower credit spreads than for example Spain. There is a myth of Iceland not paying back debt — sparked by Icesave — which is simply not true. His fact here is wrong.
2. He talked about the idea of efficient markets as something like a religious orthodoxy that people still clung to, even though it is  supposedly false and to blame for the excesses of the crisis.  He does not know what the efficient market hypothesis is. All it says is that one cannot systematically make money forecasting the markets. So he used it in a completely incorrect sense and then criticized people who still think it is  correct as being like religious fanatics. This is populism at its worst.
3. Finally, he talked about the end of the Bretton Woods system and the start of floating currencies as some sort of a capitalist plot decided on by evil bankers and implemented by corrupt politicians. Yikes. Talking about not knowing your facts. The Bretton Woods system collapsed because it was unsustainable, the bankers very much would’ve liked for it to continue. The sad truth is we have tried every arrangement for currency management and they all fail. Not because of evil plans by bankers and corrupt politicians but because they ignore economic fundamentals.

This makes me worried about everything else he says. It sounds very convincing and I am not an expert in those areas so cannot assess the veracity of his analysis. However, for someone who is as liberal with the truth, everything else he says becomes suspect. And that is sad. He is one of the sharpest public intellectuals there are and we need such people.

It’s a short piece but with important inaccuracies.

And it has done all that with very heterodox policies — debt repudiation, capital controls, and currency depreciation. It was as close as you can get to the polar opposite of the gold standard. And it has worked.

Iceland has not repudiated any debt, at least the government hasn’t. On the contrary, the government has paid back every penny owed and signaled willingness to do so in the future. The government has been rewarded by open access to international capital markets at fairly low rates — for example borrowing more cheaply than Spain. Perhaps Krugman got his facts from the fact that Iceland has refused to honor Icesave. That however is a special case. It is a relatively small amount of the overall debt created by Icelanders — private and public — perhaps hundred million euros, and the legal obligation to honor it does not exist. So it’s not debt repudiation in any sense.

The currency depreciation has been very successful to get the economy back on its feet but is a orthodox type of policy. Besides, with so many Icelanders having their dept foreign exchange linked or inflation linked, the collapse of the currency has wreaked havoc on Icelandic families and firms. It may have helped the economy, but the collateral damage is enormous.

Iceland did implement capital controls. Such tools used to be heterodox — even if they were popular in the 1950s and 60s — Iceland finally abolished capital controls in 1993. Capital controls have come back into the mainstream, and  have the full support of the IMF. How is an economic policy that is out of the IMF rulebook anything but orthodox?

The implication of the capital controls — and other measures taken by the government — has been the Iceland has just about the lowest private investment in Europe and close to the world’s highest taxes — adjusting for peculiarities of the pension system.

I don’t see this as working.

The risks impacting financial markets are attributable (at least in part) to the actions of market participants. In turn, market participants’ actions depend on perceived risk. In equilibrium, risk is the fixed point of the mapping from perceived risk to actual risk. When market players believe trouble is ahead, they take actions that bring about realized volatility. This is endogenous risk. A model of endogenous risk enables the study of the propagation of financial booms and distress. Among other things, we can make precise the notion that market participants appear to become more risk-averse in response to deteriorating market outcomes. For economists, preferences and beliefs would normally be considered independent of one another. We discuss modeling of endogenous risk and some of its distinctive features, both theoretical and empirical.

Banks operating under Value-at-Risk constraints give rise to a well- defined aggregate balance sheet capacity for the banking sector as a whole that depends on total bank capital. Equilibrium risk and market risk premiums can be solved in closed form as functions of aggregate bank capital. We explore the empirical properties of the model in light of recent experience in the financial crisis and highlight the importance of balance sheet capacity as the driver of the financial cycle and market risk premiums.

Extreme events in financial markets are often generated by shocks that are generated within the system, rather than those that arrive from outside the system. The combination of risk-sensitive behav- ior rules and the coordinated actions implied by mark-to-market ac- counting can result in outcome distributions with fat tails, even if the fundamental shocks are Gaussian. We illustrate such “endogenous extreme events” through the pricing density resulting from dynamic hedging of options and the “flash crash” of May 2010.

taken from my paper in vox, here and here.

Bank capital has emerged as a key element in the post-crisis financial regulatory reforms. Basel III is now likely to include a 7% equity-to-risk-weighted-assets capital requirement.

7% was a compromise. Some countries wanting more capital now intend to implement stricter standards unilaterally. This is making some of the others unhappy, and a bitter debate has erupted within the EU on whether individual EU member countries should be allowed to require more capital than the Basel III, and hence EU, minimums.

Capital and bank size in the EU

Measured by total assets of domestic banks, the three largest banking nations in the EU in June 2010 were Germany, the UK and France, in that order. The banking statistics come from the ECB. If we divide by GDP, the UK is largest amongst those three at 464%, followed by Germany at 337% and France at 336%. Judging by these numbers, the UK does not seem to be the outsized banking nation it often is made out to be. By including foreign banks, the numbers for France and Germany increase slightly, but the UK goes to 658%.

The ECB provides several different ways to look at bank capital. By taking the measure most relevant to the financial markets during the crisis, tangible equity/tangible total assets, the UK has the best capitalised domestic banks at 4.2% amongst the three countries, followed by France at 3.8%, with Germany having the least capitalised banks at 3.1%.

Minimum capital and Basel III: Some countries view the 7% as insufficient

In the ongoing debate on Basel III, one of the most contentious issues has been the level of bank capital. The Basel III minimum equity capital levels have been set at 7% of risk-weighted assets. Some countries have indicated they want higher minimum capital levels. One might think that the countries making the biggest public noises about problems of excessive risk-taking and speculation would be exactly those demanding higher capital. After all, higher capital directly reduces leverage and risk taking, increasing safety.

Surprisingly, it is the opposite.

• The main champions for more capital are the US, UK and Switzerland,
• The opposition is led by Germany and France.

In the US, an additional 3% may be imposed (Wall Street Journal 2011). Within Europe, the UK has similarly signalled its willingness to do the same (BBC News 2011). That would need to be allowed by EU regulations. France and Germany would like the minimum 7% also to be the maximum, following the so-called “maximum harmonisation” principle. Their public reasoning seems to be based on the public’s belief that their banks weathered the crisis better than the Anglo-Saxon banking nations. However, there is a lingering suspicion that something less straightforward is behind their stance. Germany and France may be opposing higher capital requirements because of hidden vulnerabilities in their banks’ assets. This would both make their capital ratios worse than reported above and the banks more fragile. By contrast, countries wanting more capital already might have stronger banks, partly because they have been more forthcoming in forcing their banks to recognise dodgy assets.

The maximum harmonisation principle for capital is misguided

The maximum harmonisation principle for capital would be sensible if the EU were a single financial market, homogeneous in national attributes such as bankruptcy laws and having a single European supervisory agency. In such a world, variable capital standards undermine the principle of a single financial market.

This, however, is based on a utopian view of European financial markets. After all, the EU does not have a political union, enabling a single EU supervisory agency, nor common bankruptcy laws. Consequently, the composition of assets, and treatment of assets in bankruptcy will be different across borders.

Furthermore, with each state having independent budgets, government policies and development levels, the nature and importance of banking will vary significantly across member states.

Looking at total banking assets over GDP, the relatively smallest banking state is Romania at 64% and the largest is Luxembourg at 1,964%, followed by Ireland at 929% and Cyprus at 928%. For the largest banking states, the financial sector is a significant generator of systemic risk and contributor to the business cycle. Those forces are not as strong in countries with smaller banking sectors, especially where the banks are mostly foreign.

A country with a large banking system needs different approaches to supervision than a small banking state. It needs more protection from financial turmoil and hence it would be prudent for it to require higher capital levels.

Indeed, it is sensible to vary capital levels with the relative size of the banking sector. For these reasons, the maximum harmonisation principle for bank capital is not advisable.
Those wanting low capital may have weak banking systems

The same countries that led the opposition to higher capital standards in the Basel III negotiations now oppose variable capital requirements in the EU, Germany and France.
I suspect they fear that allowing countries to impose more stringent capital standards would expose the weaknesses of their own banking systems. If an important banking nation successfully implements relatively higher capital standards, it directly signals that country’s relative banking strength and a desire not to support the banks in a crisis.

Perhaps, the real reason for the French and German opposition to variable capital standards can both be found in weaknesses in those countries’ bank assets and their willingness to use taxpayers’ money to bail out the banks.

Conclusion

The financial crisis demonstrated the need for improving capital standards, with Basel III a step in the right direction. Countries with large financial systems need to have the freedom to impose stricter controls on risk taking than is the norm. For these reasons, an EU maximum harmonisation for bank capital would be the wrong step.

References

BBC News (2011). “EU block on making banks safer”, 16 June.
Wall Street Journal (2011). “Lenders Dig In on Rules”, 16 June.

second part from this post

The appropriate use of risk models: Part II

Risk models are used in four different (though overlapping) situations:

• Routine understanding of risk by banks and supervisors;
• Routine management and control of risk by a banks and trading desks;
• Analysis of systemic and regulatory risk;
• Management and control of systemic risk by supervisors.

We consider these in turn.

Routine understanding of risk by banks and supervisors

The easiest situation is where risk models are used to understand rather than constrain risk, and where risk assessments concern routine rather than extreme risks hence making large amounts of relevant data available.

Because portfolios are not constrained by the models, error maximisation is not an issue, and complex models with good expressive power and a good fit to historic data can be employed, while large sample sizes keep problems of data snooping and over-fitting to a moderate size.

These circumstances are ideal for off-the-shelf models and they should be expected to perform well in this role. Any failures should not be systemically important. Unfortunately such uses of models, although common in academic studies, are likely to be rare in practice.

Routine management of risk by financial institutions

When someone in industry models risk, it is generally to control risk. This introduces error maximisation. The more risk models are used to constrain risk taking, the more fragile error maximisation makes them. Consequently, one should be especially careful to avoid the problem of data snooping.

This suggests particular criteria for evaluating models used to control risk. They do not need fit historic data particularly well. Instead, they need to be robust against currently-unknown future mistakes. A good in-sample fit is not only irrelevant, but even dangerous because it gives more scope for error maximisation. This suggests that models used to constrain risk should be substantially simpler than models used to understand risk. We think this distinction is not widely understood.

Supervisors should be particularly cautious about demanding the use of similar risk models across multiple institutions because by doing so they increase the likelihood that all institutions suffer a failure of risk control at the same time, elevating an individual problem into a systemic one. This was noted by Danielsson and Shin (2003) in their discussion of endogenous risk.

Analysis of systemic risk

The area of systemic risk brings further challenges. Here the question of interest is not the risk of financial institutions failing, but rather the risk of cascading failures. Consequently, a reliable systemic risk model needs to capture the risk of each systemically important institution, as well as their interactions. This will be challenging since such models will reflect the financial system as it is, not as it would be if policymakers acted on the model. Since the models are endogenous to the system they model it is impossible to avoid a substantial subjective judgement on what influence they may have.

Systemic risk is concerned with events that happen during crisis conditions, looking far into the tails of distributions. This makes the paucity of relevant data a major concern, dictating the use of very simple models to keep the adverse effects of over-fitting to reasonable levels. In addition, any reliable systemic risk model needs to address the transition from non-crisis to crisis.

Incentives make the process difficult. During tranquil times, the risk of extreme events is a low priority. Extreme outcomes happen only rarely, perhaps once a decade, and bonus and employment cycles are much shorter than that. It is not in the interest of risk takers, speculating with other peoples’ money, to be overly concerned with extreme risk. Even if the financial institution or the supervisor does have such concerns, such risk taking is difficult to detect when concealed in a turbid alphabet soup of derivatives. In the final analysis, the likelihood of extreme events is often impossible to detect with any model.

Daily 95% or 99% risk levels, such as those in the Basel market risk accords, are of very little direct relevance for systemic risk, and further introduce the problem of error maximisation. We therefore disagree with the widespread assumption that successful models for routine risk can be expected to perform well in systemic risk forecasting. This applies to many systemic risk models currently being proposed by government institutions.

Ultimately, the difficulty of the systemic risk problem suggests that supervisors working on systemic risk should be wary of statistical models of extreme market outcomes. The models may provide a number labelled “systemic risk”, but this does not mean that the number has any meaning. Excessive belief in statistical models will lead supervisors to defend against obsolete threats and leave them blind to the new.

Here be dragons: The challenge of controlling extreme risk

Medieval mapmakers often noted the risk of an unknown kind by the notation “here be dragons”. Attempts at controlling extreme risk should come with a similar warning. Just like the sailors of yesteryear, financial institutions will go into unknown territories and, just like the map makers of that era, modern risk modellers have very little to say.

Financial institutions, willingly or not, will assume extreme risk. This cannot be prevented with any cost-effective methods. Nevertheless, the repeated occurrence of extreme events in financial markets implies that such risks need to be contained and managed. After all, following a crisis event there is generally strong political pressure on supervisors and financial institutions to prevent recurrence.

This however leaves open the question as to the extent to which it can be accomplished. We argue above that it is difficult to forecast extreme risk because of over-fitting. Basing risk constraints on the resulting estimates adds the problem of error maximisation, placing any supervisor seeking to constrain extreme risk taking in a very difficult situation.

Financial institutions and supervisors seeking to control extreme risk-taking should be careful in their use of statistical models, and certainly not use them simply as a substitute for trying to understand what trading strategies are being employed and how they might contribute to some future risk. These models must face all the challenges of those used to understand systemic risk, and in addition will face error maximisation.

Models are of course necessary but they should be extremely simple and founded on very basic measures of asset size and risk. They should have very few (or ideally no) parameters estimated from history, because each parameter increases the scope for data snooping and error maximisation. They must make a limited and wary (or no) allowance for hedging, because in a crisis hedging can be expected to fail.

Unfortunately, the current thrust of regulation seems to be to be in the opposite direction, mandating the use of powerful and expressive models that may provide a good fit to historic data, even deep in the tails, and then use those models as a rigorous constraint on risk taking across many portfolios. This is exactly the most fragile approach.

Some suggestions

As we started this article with a challenge from a risk manager, we want to use this analysis to make specific recommendations on the use of models.

Most importantly we want to underline the need for understanding. Risk estimates do not exist in a vacuum; they are made for some purpose and based on some model. Sensible estimates cannot be made unless both are understood. Ideally risk managers should create their own models as this is the best way to understand the model’s limitations. Outsourcing risk-model creation is outsourcing a key component of risk control and if it is done then substantial efforts must be put into understanding and monitoring the work that has been done.

Any risk forecast should come with robust analysis of forecast uncertainty. This might take the form of the fan charts used by the Bank of England for inflation forecasts. Forecast uncertainty should incorporate statistical uncertainty within the model (such as parameter standard errors), model risk (uncertainty created by using the wrong model) and well as an estimate of the bias introduced by data snooping. Existing statistical methodology allows for such calculations, it is just a matter of mandating their use. Any serious discussion of risk should incorporate explicit estimates of uncertainty.

Of course providing sensitivity analysis for risk forecasts does create problems, particularly making the interpretation of the results harder to communicate to senior management. At the end of the day practical decisions have to be made and few decision-makers are comfortable with the multi-dimensional integrations required when using full distributions rather than point forecasts. Nor can they be expected to enjoy the explicit recognition that every decision may be wrong.

Unfortunately this is the reality of decision making, and obscuring it may make life more comfortable but corrupts the decision-making process. At the very least, there should be lively discussion of uncertainties between risk professionals and any software providers involved, both within individual forms and also in supervisory agencies.

Extreme risk creates additional challenges due to the paucity of relevant data, the correspondingly highly-subjective element in the model, the length of time required to falsify an incorrect model and the seriousness when an extreme event occurs. There is a natural tendency in the industry to sweep problems of extreme risk under the carpet. After all, if the portfolio or the institution does not blow up on my watch, why should I care? Relying on statistical models to assess extreme and systemic risk is likely to provide the false belief that the problem is under control, such as persisted for a decade after Alan Greenspan’s “Irrational Exuberance” comment of 1996.

Macroprudential regulations should focus on prevention and resolution of systemic events. This is very different from trying to smooth out risk-taking on a day-to-day basis, and the latter may well be counterproductive if the result is to lower volatility at the cost of producing fatter tails. More attention should be paid to preventative measures that are model-free or depend only on the very simplest models, such as restrictions on loan-to-value ratios, minimum equity capital based on total (not risk-weighted) assets, such as the leverage ratio, and the Basel 3 liquidity constraints. The on-going work on living wills and resolution is very encouraging; even if much remains to be done. (See e.g. Bassani and Trapanese 2011).

Conclusion

Financial risk models, statistical and non-statistical, are essential for the functioning of financial markets and it would be impossible to manage risk without them. However, there is a tendency both by financial institutions and by supervisors to overstate the reliability of models and underplay their dangers, so it is important to identify their limitations.
We have identified several different reasons why risk models fail in practical use. This allows us to make constructive recommendations on where models can be used with confidence, where problems are likely, on what characteristics models should have and how they should be used, and on how the nature of the intended application will influence all these considerations.

References

Bassani, Giovanni and Maurizio Trapanese (2011), “Crisis Management and Resolution”, forthcoming in M Quagliariello and F Cannata (eds.), Banking Regulation, Riskbooks..

Danielsson, J and HS Shin (2003), Endogenous risk. In Modern Risk Management — A History. Risk Books.

Danielsson, Jon and Robert Macrae (2011), “The appropriate use of risk models: Part I”, VoxEU.org, 16 June.

The appropriate use of risk models: Part I

Financial risk models have been widely criticised for both theoretical and practical failures, especially during the recent financial crisis. In spite of this, all proposals for reforming model use have been resisted. This is not surprising given how deeply ingrained models are in the practice of finance.

Such sentiments are eloquently stated in the conclusion of a comment on a recent Vox article;

“As a risk manager I fully recognise the shortcomings of any model based on or calibrated to the past. But I also need something practical, objective, and understandable to measure risk, set and enforce limits, and encourage discussions about positions when it matters. It is very easy to criticise from the sidelines – please offer an alternative the next time.”

Jan-Peter Onstwedder, Comment on Danielsson (2011)

Our aim here is to respond to the challenges such as those in Jan-Peter’s comment by making specific proposals for how risk models should be used in practice, and identifying how problems with models can be avoided. For a background on the theoretical aspects of risk models see Danielsson (2009, 2011). A practical analysis of models can be found in Macrae and Watkins (1998).

Nature of risk and of risk models

Financial risk is a forecast, not a measurement. Every risk forecast is an uncertain assessment of the underlying risk factors, often with wide confidence intervals, resulting from parameter uncertainty, model error and data snooping, and usually containing an uncomfortably large subjective element. Even nonparametric estimates will require choices such as estimation period.

Financial risk can only be understood in terms of a model. It may be a formal model, but whenever a user adopts some rule for controlling risk there must be a model implied by the rules adopted. For example, lending ratio restrictions imply a simple model that more bank loans lead to greater risk. A more complex model incorporating different levels of loan risk and operational risk is implicit in the Basel II risk weightings.

Despite model dependency and uncertainty, there is a tendency by end users to perceive numbers representing risk as coming from a scientific measurement – using a Riskometer in the language of Danielsson (2009)– rather than from an uncertain statistical procedure. Users need numbers they can use to convince their boss, client or regulator, so users of risk models prefer “objective” risk forecasts whereas forecasts accompanied by qualifications and uncertainties appears less objective.

We suspect this leads users to prefer commercial risk software that provides a single number, unencumbered by confidence intervals even though this makes it particularly hard for users to evaluate the reliability of off-the-shelf models. Where confidence intervals are estimated, their reliability is often suspicious. This is succinctly illustrated by David Viniar, Goldman’s chief financial officer, stating: “We were seeing things that were 25-standard deviation moves, several days in a row” (Financial Times 2007). This can only mean that Goldman grossly underestimated its standard deviations, making confidence intervals far too tight.

Why are uncertainties in risk forecasts so high?

There are several reasons why uncertainties in risk forecasting are higher than is usually assumed:

• The model estimation period is too short;
• There are structural breaks during the estimation period;
• Data snooping and model optimisation occur;
• Portfolios are optimised, maximising errors;
• It is often necessary to forecast extreme risks.

Since the first two issues are well known, we want to focus on the final three.

Data snooping and model optimisation

Every student of econometrics is taught the danger of data snooping. If we run a single regression, we get correct confidence intervals for parameter estimates and forecasts, subject to certain basic assumptions. If, however, we arrive at the same model as a result of optimising a number of explanatory variables and model specifications these assumptions are violated and the confidence intervals will be underestimated. The more complex the model and the smaller the dataset, the larger the underestimation becomes.

The misleading inference that data snooping can cause is demonstrated by Sullivan et al. (1999), who show that apparently statistically significant technical trading rules are not significant if confidence intervals are calculated correctly, taking into account the search for the best model.

Similar effects are at work in risk forecasting. Risk models are routinely validated by back-testing, that is, by examining how well a model forecasts market outcomes that have already happened. If the model performs badly it will be changed, and the end result is certain to perform well in-sample, over the back-testing period.

Such common approaches to risk modelling tell us more about the level of model optimisation than about how the model will perform out-of-sample in the future. Most risk models in practice appear to us to overemphasise their ability to fit past events, rather than out-of-sample risk forecasting. Risk models must be parsimonious, and tested over a variety of market turmoil if they are to minimise the problem of data snooping and model optimisation. The model best at forecasting is unlikely to be the best at capturing historic events with great accuracy.

This imposes a fundamental limit on what risk systems can achieve, especially in a crisis, because parsimonious models cannot deliver great precision but non-parsimonious models are likely to fail out-of-sample.

Portfolio optimisation and error maximisation

A related problem arises from the use of risk models in portfolio optimisation and risk control. Where risk models are a direct input into trading decisions, providing hard constraints on risky positions, the underlying trading process and portfolios will in all likelihood adapt to and exploit model weaknesses.

This problem arises since traders optimise portfolios towards low reported risk (or equivalently low capital usage) and high returns, causing trading decisions to become biased towards assets with under-forecast risk. In other words, the trader maximises exposure to the part of the asset universe with biased risk forecasts, maximising the impact that this error has on the portfolio. Such error maximisation can affect individual trading positions, institutions, and even the financial system as a whole, as illustrated by the recent crisis.

Prior to the crisis, many structured credit products, such as certain CDO tranches, had undeserved AAA credit ratings. As many investors correctly perceived the risk of such AAA tranches as higher than the risk of corporate AAA bonds, their yields were typically somewhat higher than corporate AAA yields. This in turn made such tranches attractive to less sophisticated investors who evaluated risk solely on the basis of credit ratings.

It is not the size of the pricing bias nor the magnitude of the event that is the main culprit here; the CDO market is a relatively small part of total financial assets. The problem is that the presence of tightly-binding constraints based on inaccurate models of risk (and the consequent error maximisation) motivated certain financial institutions to acquire large exposures to these assets. This led to concentrated losses with damaging systemic consequences.

Error maximisation, as an active risk management leads to reduced volatility and fatter tails. The risk in common events is better managed, at the expense of bigger and more frequent extreme events. The more rigorous risk models are used to constrain positions, the more errors will be maximised and the more dramatic will be the consequences when the errors are eventually revealed.

All risk models contain errors and are thus vulnerable to error maximisation. The more widely a model is used and the more tightly a constraint binds, the worse the error maximisation becomes. This argues for heterogeneity in risk models. In the worst case, where a single model or approach is given regulatory force and applied as a hard constraint to many portfolios, a small problem in micro-prudential regulations may be elevated to a systemic level.

Risk managers are well aware of the potential for error maximisation. However, we suspect this is not well understood by senior management nor properly considered by designers of financial regulations.

This imposes a second fundamental limit to what a risk system can be expected to achieve, because risk systems used to constrain portfolios will have been compromised by the implicit optimisation of portfolios to contain assets for which risk systems underestimate risk. Risk systems that have been used to constrain positions will always prove unreliable in a crisis.

Extreme risk forecasts

Perhaps the greatest need for models is in the forecasting of extreme risk or tail risk, especially during periods of financial crisis and extreme market turmoil. This however, is the area where risk models are least reliable because the effective sample size of comparable events is very small. At worst there might be one observation or even zero when we wish to consider events not yet seen.

Over the past half century we have observed fewer than 10 episodes of extreme international market turmoil. Each of these events is essentially unique, and apparently driven by different underlying causes. Trying to get an overall idea of the statistical process of data during those episodes with fewer than 10 episodes of turmoil, all with different underlying causes is difficult to the point of impossible. While it might be possible to construct a model fitting 9 crisis events in a row, there is no guarantee that it will perform well during the 10th.

Nor does it seem likely that we can get much information about price dynamics during turmoil by using the non-crisis data that makes up the bulk of available information since there is ample evidence that market dynamics are very different in times of crisis. Market lore suggests that in a crisis traders rely more on simple rules of thumb (such as “all stocks have a beta of one”, or even “cash is king”) than in more nuanced normal times. This is supported by academic studies, such as Ang et al. (2002), showing that that correlations go to one during crises (manifestation of nonlinear dependence), because of incentives to trade out of risky assets into safe assets when risk constraints bind, causing a feedback between ever higher risk and sharper constraints (see Danielsson et al. 2010).

This is the third fundamental limit to what risk system can be expected to achieve. Regardless of how much data we have, there is never enough to reliably estimate the tails. This is why models for extreme risk can be expected to fail during market turmoil or crises.

Tomorrow we consider how the intrinsic shortcomings of risk models matter for their four main uses. We also make some suggestions on how the financial industry and supervisors should use models in practice

References

Danielsson, Jon (2009), “The myth of the Riskometer”, VoxEU.org, 5 January.

Danielsson, Jon (2011), “Risk and crises”, VoxEU.org, 18 February

Danielsson, Jon, Hyun Song Shin, and Jean-Pierre Zigrand (2010), “Risk Appetite and Endogenous Risk”, Financial Markets Group Working Papers.

Macrae, Robert and Chris Watkins (1998), “A Disaster Waiting to Happen”.

Sullivan, Ryan, Alan Timmermann and Hal White (1999), “Data–Snooping, Technical Trading Rule Performance, and the Bootstrap”, Journal of Finance.

Ang, A and JS Chen (2002), “Asymmetric correlations of equity portfolios”, Journal of Financial Economics, 63(3):443-494.

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