Middle Kingdom, Middling Growth

...packed with the asymmetric possibility of a catastrophe

A recent FT alphaville overview on the flurry of bad news on China. PMI's are also looking quite poor. Josh Brown, in his review of the Barron's article, touched on a key quote:

"A falloff in demand for steel, cement, and copper would lead to heavy layoffs. He reckons that some 25% of all Chinese steel consumption goes into residential real estate."

This is very similar to what I noted about a month ago with the varied, unknown connections that make the Chinese economy so fragile. Besides the direct job effects, copper prices have a dangerous relationship with collateral for financing, thereby creating the possibility for a financial mini-crisis in commodities along with the brewing housing debt problems. The Chinese economy is in a set of asymmetric straits. And for as much as the Chinese government is a "pragmatic" one, the serious suits at the top of the Politiburo are not phasing out state owned enterprises in favor of more efficient channels for growth, At such a juncture, perhaps central government easing can restore growth to above 8% and hold it relatively steady. But in the process, fault lines are deepened, and catastrophe looms. You know how good it can get, but you have no idea how bad it can become. So China will be fine, or it will go through crisis. And you never know where a single mistake can lead you.

Swiss Fragility

Swiss watches aren't fragile; but can the same be said for the currency floor?

Evan recently had a post looking back on the Switzerland issue, which made me look back at our previous discussion.  In the spirit of Evan's proposition reflection, I'm also going to elaborate on a few of my arguments and look at where they're still true (often in different ways) as well as proven wrong (no surprises).

As I noted in my addition to the previous post, a lot of my previous arguments about the instabilities of a currency peg are not as relevant to the Swiss situation. The Swiss intervention is a floor on the exchange rate, and not a fixed peg. The SNB policy is to intervene on foreign exchange markets so that the Franc/Euro exchange rate can not go below 1.20. In effect, the 1.20 Franc/EUR exchange rate is the strongest the SNB will allow the Franc to be. As a result, the Swiss are not dealing with inflationary pressures as a result of the currency floor, and there's no tension between the domestic monetary policy goal of stabilizing NGDP and the external goal of limiting real appreciation.

Moreover, I do agree with Evan's broad argument that the power of expectations substantially reduces, but does not eliminate, the need for the SNB's forex interventions. However, I'm not quite as optimistic as Evan on the longer term effects of the exchange rate floor. I can't deny that the economic indicators look good: 2.8% annualized GDP growth, 3.2 percent unemployment, etc. But in spite of these positive signs, I want to make a general comment about discussions about expectations and cautionary notes on the fragility of such an arrangement.

First is that expectations cannot work in every instance. My arguments on the instability of currency pegs are a perfect example. Pegs can't function on expectations because speculators can push the bank off the edge because there's not an infinite supply of foreign reserves to defend the peg. While the SNB's policy is not a peg, this comparison leads to an important theorem for expectations arguments. Expectations can only be used as an argument if the policy would work even without expectations. In the case of the currency floor, the forex intervention would stop appreciation even if speculators didn't have expectations of future policies. The SNB could just keep on printing francs and depreciate the currency. If the speculators didn't take into account that they were overpaying for francs, they would be naturally selected out of the market by speculators who would bet that the SNB's floor will hold. Thus, if the SNB commits, they can hold the exchange rate greater than or equal to the 1.2 floor. Speculators, knowing this, would then stop speculating. In the case of unconventional monetary policy, asset purchases and lower IOR in and of itself can raise growth through portfolio balance and hot potato effects. Expectations can help augment these policies because the market would do the work for the central bank, increasing monetary policy's effectiveness. In each of these circumstances, expectations make the policy more powerful, but in and of themselves cannot make impossible policies possible. Expectations are powerful, but we need to make sure to use a rigorous set of criteria before we start applying the argument everywhere.

Second, such large scale asset purchases on the part of the SNB to maintain the peg represent an important form of instability. While I was not prescient enough to make the argument in full, I did comment on the danger of such a large balance sheet filled with foreign currency denominated assets. As Evan notes, a lot of forex intervention from the SNB is the result of absorbing demand for a safe haven currency. Thus, unless policy changes, we should expect the balance sheet to get larger and larger.

As the size of the balance sheet increases, we have to start worrying about fragility, or any ripple effects across international financial markets. To illustrate the problem, imagine a "fundamental" rate path that charts the value of the franc in the absence of a floor from the SNB. This fundamental value would also take into account the demand for a safe haven asset. Given the power of the floor, we can safely assume, currently, the fundamental value is below the floor.


However, if, for whatever reason, the fundamental value shoots above the floor (when the orange part goes above the blue), we can anticipate a sudden large amount of selling and revaluing of assets. All of a sudden, what was once at a fixed exchange rate is now moving. Because the SNB's balance sheet is so large, this shift could have massive implications for the balance sheets of other governments.

The most pernicious part of this shift would be that, given the suppressed volatility from the floor, there would be no way for the SNB to tell when we should start to get worried. Even if the fluctuation was pure static, like that shown in the graph, there would be a massive signal confusion problem. Policy makers could guess, but prediction is often quite difficult. It would also be unlikely that other indicators, such as unemployment or inflation, would be update quickly enough to keep up with changes in forex flows. This represents a key form of fragility that we need to worry about in all expectations regimes. If, for some reason, the regime changes, many arrangements that depended on the previous regimes have to be recreated, with sometimes drastic consequences.

To get around such issues, the exchange rate needs to be less about maintaining some floor and more about maintaining a favorite target, such as forecasts of NGDP growth. This way, the market is allowed a natural level of volatility, and policymakers can use this volatility to gauge the state of demand for the currency. Additionally, this natural, day to day volatility would caution those who hold the Franc from depending too much on its 1.2 floor. Alternatively, increasingly draconian capital controls could be used to change the fundamentals of holding currency in Switzerland, thereby limiting capital inflows while allowing volatility in the exchange rate. However, such an approach is subject to leaks, and given the manual smuggling of currency, is likely to fail.

Third, and as a corollary to the second point, the question we need to ask whenever we talk about regimes that fix certain values is "what are the costs?" As Miles Kimball noted, central banks can do a lot of things, so our attention needs to shift to the collateral effects of such actions. And as Bastiat reminds us, we need to look at that which is hidden in addition to the more obvious effects. While a lot of the more mainstream discussions focus on welfare costs of volatility, I worry more about fragilities in the spirit of Taleb or fault lines in the spirit of Rajan. I've commented on this line of thought in the context of NGDP targeting (here and here), and I think it has an important role in any kind of system that suppresses volatility, such as the exchange rate peg.

I can't quibble about the SNB's effective block of the Franc's appreciation, but we must qualify its success. Beware manufactured stability, especially when it creates fragilities and uncertainties that we aren't prepared for.

The Danger of Promises

Promises are powerful, so don't make a promise you can't keep

Evan Soltas had an interesting post on the power of promises in the context of currency bands and monetary policy, but we should also remember that banks shouldn't try to make promises they can't keep. We all should be very worried when we see graphs that show sudden decreases in volatility, as Evan shows in his post. Whenever policymakers suppress volatility, we need to wonder where those pressures have gone, and why they have disappeared. More often then not, manufactured stability leads to calm periods punctuated by sudden change; they become Type 2 extremistan regimes, as pictured below:

So why did the currency peg work? The peg only makes sense in the context of Scott Sumner's argument that the currency peg is a form of monetary policy commitment. The undervalued currency increases aggregate demand, thereby filling the output gap. However, it should be observed that, given the undervalued currency, maintaining the peg leads eventually to above trend NGDP growth and an economy that's running "hot". However, the "hot" economy would call the currency peg into question. This is a classic example of Mundell's policy trilemma. The SNB cannot pursue an exchange rate peg, independent monetary policy, and capital mobility at the same time. Conditions are stable now only because the exchange rate peg matches the objective of an independent monetary policy. However, once the output gap starts to narrow the credibility of the exchange rate peg will be questioned.

This is where the expectations channel starts creating weird dynamics. If the market expects the SNB to pursue monetary policy that prioritizes internal conditions, then the market should expect that currency to appreciate in the future as the output gap is filled. However, because of inertial central bank policy, this will only occur when the currency is undervalued to such an extent that it is no longer feasible for the central bank to maintain the peg. At that point, we should expect to see a very sudden adjustment as the SNB comes under fire from speculators. This then creates a vicious loop, as the SNB's attempts to maintain the peg only increase the supply of currency, of which speculators buy increasing amounts as they now know that the currency will appreciate.

When the SNB is forced to rebalance the currency, it will shake up markets as it unwinds its large balance sheet of foreign assets. Since the bank would have been accumulating these assets for a long time, their sudden liquidation is likely to be a "fat tail" event, which, on one hand may not cause that much damage, but on the other hand may cause positive feedback loops to devastate markets in unknown ways.

While this analysis is in the specific context of the Swiss central bank, this argument has implications for NGDP targeting in developing countries as well. The problem with the SNB's currency peg is that it prioritizes one objective (exchange rate) over all others (including NGDP). However, when domestic politics rears its head, a internal measures such as NGDP will end up trumping external measures such as the exchange rate. The crisis arises from a sudden reversal in priorities.

This exchange rate-NGDP tension is very important for developing economies. If these countries pursue capital mobility, then they may need to compromise part of their monetary policy to maintain an exchange rate band. Thus, this is another source of possible fragility in a NGDP target, as other objectives, such as the exchange rate, suddenly come to the forefront.

Edit: Evan Soltas gave me a further explanation on how the "peg" is really a floor, as well as an explanation of the general macro conditions in Switzerland. I failed to take the time to analyze them, so the conclusions are slightly different. There's still possibilities of non-linear dynamics, and those are explained in the comment thread. I've also written a new post reflecting back of these issues here.

Market Lessons, Fragility, and Debt

How does the market make sure businesses learn the right lesson?

(Photo credit to Seven Bedard)

What is the invisible hand?  How does it push businesses towards the right prices, the right contracts, the right decisions?  Introductory supply and demand models suggest that businesses are always profit maximizing, thus changes in the market induce changes in business behavior to maximize that profit. However, this is, at best, a theoretical abstraction. While businesses may intuitively know the law of demand, they do not have some demand curve available to them for analysis. To optimize price, they tinker until they find a satisficing option. Given enough tinkering, the firms eventually reach a price that is profit optimizing. The trial and error of individual firms eventually crafts a market equilibrium.

But how does this work on a larger scale, with more complicated decisions? How do businesses decide between the right balance of debt and equity? How do they determine what their production function to determine the "optimal" amount of investment? What's the "expected value" of long term research to power the company? These are all unknown values; how does the market then move firms to act optimally with respect to them?

According to an evolutionary approach, the market does not teach or push existing firms to the optimal levels, rather those who fail to reach the optimal levels are naturally selected out of the market. Firms don't need rational expectations about price growth, all that is necessary is that the market is deep enough that the irrational expectations are purged from the system. Along with the adaptive market hypothesis, this suggests that markets only work as our idealized models would predict if and only if the market is fast paced with strong pressures that punish "irrational" strategies. These markets need to have constant sources of volatility so the invisible hand can keep on pushing businesses in the right direction. Market don't teach, they destroy. In the end, efficient markets are only the result of survivorship bias, as every inefficient market loses firms until the remaining ones act "rationally".

If evolution is why markets equilibrate, then we need to be especially concerned about systemic crises and debt. Taleb warns about the impact of debt crises in his list of steps towards a Black Swan free world;

5.  Counter-balance complexity with simplicity. Complexity from globalisation and highly networked economic life needs to  be countered by simplicity in financial products. The complex economy is already a form of leverage: the leverage of efficiency. Such systems survive thanks to slack and redundancy; adding debt produces wild and dangerous gyrations and leaves no room for error. Capitalism cannot avoid fads and bubbles: equity bubbles (as in 2000) have proved to be mild; debt bubbles are vicious.  

From his description, one can see many channels through which debt disrupts the evolutionary process in markets. If there's no room for slack and redundancy, and if these crises go systemic, there would be no firms left untouched by the crisis. As a result, no firms are left and there's nothing left that the evolution in markets can teach them. Systemic crises destroy the history record of firms with better models for long term growth, so the evolutionary pressure evaporates.

Moreover, the buildup of debt suppresses short term volatility in exchange for long-term blowups. Debt financing can tide you over in the short run, but eventually debt catches up with businesses, resulting in a catastrophic, if not systemic, collapse later on. This suppression of volatility also crimps the ability of markets to force firm evolution, as there is no longer the short term volatility to promote tinkering. Given these diverse channels in which debt interferes with the evolutionary equilibrium of markets, the fragility imposed by debt should draw greater attention in the regulation of complex economies.

Capital Spillovers: Towards a New Regime

A look at the externalities from capital controls and potential effects on international finance


The focus of this post is going to be on the arguments in a recent NBER working paper on how capital controls in one country should be evaluated on an international level.

An interesting development in the analysis of capital markets in the past few years has been a changing paradigm on the use of capital controls as macroprudential policy. Through empirical analysis, we've found various channels through which capital controls function. As per the working paper:

Some economists and policymakers have recently become more supportive of controls on capital inflows, particularly if they are aimed at limiting the appreciation of overvalued currencies and reducing financial fragilities resulting from large and volatile capital flows. This support has been bolstered by theoretical work showing that taxes on capital inflows can improve a country’s welfare by reducing negative feedback effects due to capital flow volatility (Korinek, 2010 and Jeanne and Korinek, 2010) or by adjusting the terms-of-trade to shift consumption across periods (Costinot, Lorenzoni, and Werning, 2011). This theoretical work has been supported by empirical work showing that even if capital controls cannot significantly affect the total volume of capital inflows, they can improve the country’s liability structure and increase its resilience to crises (i.e., Ostry et al., 2010).1

Yet the analysis hasn't really focused on external effects of capital controls of country on others. This is an important field of study because, as the study of customs unions or financial contagion has taught us, the effect of controls or restrictions change significantly in a second-best world populated by other states with their own policy regimes. Thus, any possible externalities from one country's capital controls are extremely important in the formulation of policy.

The paper looks at 'moderate, market-based controls on capital inflows in a country which previously had a relatively open capital account." In effect, it shies away from large scale capital controlled regimes like China.  Also, these types of capital controls are unlike the command-and-control regulations that China uses in stopping capital mobility. Instead of a quota, the controls analyzed in the paper make capital harder to move on a per-unit basis. While Chinese capital controls can be represented by a levee, the capital controls analyzed in the paper are the equivalent of making the hill a bit steeper or moving to higher ground. For more rigorous empirical analysis, the paper looks at Brazilian markets, as they are relatively deep and have been subject to multiple changes in capital restrictions over the years. The authors use the Emerging Portfolio Fund Research (EPFR) to analyze changes in mutual and bond funds.  In addition, they interview investors from a variety of backgrounds to learn about how market practitioners respond to capital controls.

An interesting nugget from the empirical analysis is that a large part of the impact from Brazilian capital controls arises from the expectation about future policy. Capital controls in Brazil lower investment in Brazil, but also in other countries that have a higher risk of implementing more capital controls. The specific wording from the paper:

Given these significant portfolio effects of capital controls on investor allocations to Brazil, it is not surprising that there are externalities on portfolio allocations to other countries as well. Confirming comments in some of the investor interviews, we find that these spillovers are heterogeneous and depend on country characteristics and the fund manager’s strategy. When Brazil increases its capital controls, investors increase their portfolio allocations to other countries that are closely linked to growth in China (through commodity or regional exports). There is also mixed evidence that investors may increase their portfolio allocations to other countries in Latin America. At the same time, increased capital controls in Brazil cause investors to reduce their portfolio allocations to countries that are perceived to have a higher risk of following Brazil’s example and implementing new controls (including countries that are traditionally open but recently imposed new controls as well as countries that traditionally have extensive restrictions on capital mobility). These results confirm that much of the effect of controls is from signalling, i.e. changes in investor expectations about government policy, even for countries that are not concurrently adjusting their capital controls (6). 

This shift in expectations then has implications for whether capital controls can be targeted. If controls cause markets to judge an entire government as heterodox, then capital flows as a would reduce all forms of foreign investment. Additionally, Brazilian capital controls may change global expectations about policy for capital controls.  This may change the allocation of funds at a global level as well.


The shift towards Chinese investments in the face of Brazilian capital requirements is quite interesting. This suggests that there's something offered by investments related to China that similar to that of Brazilian investments. This relationship is probably built on the BRICs of emerging markets, but these similar flows weren't recorded as going into Russia or India. Brazil is also unique in its central placement in South America, which is probably the reason Brazilian capital controls pushed a substantial portion of the new flows into other Latin American countries. Although their development has not kept pace with Brazil, they are likely exposed to similar risks with respect to commodity price shocks on the global marketplace. As a result, an investor might decide to shift away from Brazil, which has capital controls, to another Latin American country which would offer a similar type of investment, but with a higher yield due to lower capital controls. The authors use these spillover effects of capital controls to justify more international coordination:

If capital controls shift vulnerabilities from one country to another, this “bubble thy neighbour effect” should be incorporated in any reassessment of the desirability of capital controls. Moreover, if several countries simultaneously adopted controls as part of a standard “policy toolkit”, or if a single large country adopted more stringent controls than Brazil’s small tax analyzed in this paper, the externalities could be substantial. This does not necessarily mean that capital controls will reduce global welfare and should always be avoided. Instead, these results support a role for international coordination or oversight of the use of capital controls to avoid a “bubble thy neighbor” effect which could lead to retaliation across countries and reduce global welfare (6).

The argument about "retaliation across countries" exposes an important weakness of capital controls. If capital controls function primarily by pushing hot money flows onto other countries, a global regime of capital controls is likely to have the same effect that capital controls have on a country-by-country basis. In effect, the fallacy of composition strikes again, as if every country imposed capital controls, capital controls would no longer address vulnerabilities in finance. Rather, they would just lower the global return on capital.


Section 2 of the paper moved on to investor surveys of attitudes towards capital controls. They reveal a wide diversity of views on capital flows.  While some investors were neutral (“cost of doing business”), quite a few of them interpreted capital controls as signalling something more fundamental about the country (“anti-investor bias of the country,”  “an increase in policy uncertainty in the future,” “a government that does not know what to do,” or a “lack of stability in economic policy”) that constituted "a draconian policy".  Other investors had positive views, as "controls showed the country was addressing potential vulnerabilities due to a rapid expansion of credit related to capital inflows".  If capital flows substantively affect future expectations of capital stability, these yield expectations should be integrated into the design of policy.  Yields could be used as a tool to look at market expectations of the future path of policy.

Capital controls also have heterogenous effects on different investors.  Equity investors face higher levels of volatility, so taxes on the order of a few percentage points of return were not very worrisome. On the other hand, for those who were dealing with fixed income assets or had absolute thresholds to reach, the tax on incoming capital had a major effect.

The surveys also reveal that capital controls, as currently implemented, affect the economy with long and variable lags.  This would take place in spite of advanced knowledge of when the controls would be implemented.  There were four key reasons for this:

1. Expectation lag - even though capital controls changed the expectations of future policy, any given change could only be part of a "broad assessment" of investments in a country.  As a result, one capital control would not immediately affect a change in investment strategy.
2. Institutional lag - in many institutions, fund allocations depend upon decisions of large committees that go through a "lengthy process of meetings, documentation and approvals" (10).  As a result of the internal transaction costs of making the decision, capital controls don't have immediate effects.
3. Coordination lag - the effect of capital controls extends beyond one firm.  Rather, investors require information about the actions of other agents to make a decision; they can't ex-ante coordinate to figure out how much each firm will invest.  As a result, responses to capital controls take time.

All of this shows that investors respond in very different ways to capital controls; the image of a representative investor investing symmetrically with the market is an illusion.  Policy then needs to be designed carefully to take into account these different types of investments.

Overall, this paper has notable implications for a global shift away from financial fragility through capital controls. The signalling argument means there's room for rule based policy when it comes to macroprudential regulation.  To clarify what the government wants to do with capital flows, they can create a "macroprudential regime" to try and anchor investor expectations, thereby creating an environment more conducive to growth. 

Also, the international spillovers of capital flows make the dynamics of adjustment to an international landscape dotted with capital controls unpredictable. As more and more large economies move towards capital controls, two scenarios could evolve. One is that the states without capital controls would be seen as freer to capital flows. This would create strong incentives for the remaining states to implement controls so as to prevent the new flows of hot money from coming into their polities. Even without international coordination, there would be a snowballing effect towards a new regime on capital controls. On the other hand, if the expectations channel was strong enough, the remaining states could go without controls as investors already expect them to be willing to take measures to moderate capital flows. In effect, the actions of the first movers would immunize the rest. Under this scenario, the first movers would be providing a public good with their decision to move first. As a result, there would be an inefficient level of capital controls. I'm not sure which one is more correct, but either way they make the study of international finance very exciting as development marches forward.

A Look Back Through the Lens of Complexity: "Bigger is Better"

When fears about the Euro were not always so well developed

Recent developments in Europe have created fears that Greece will soon exit the Euro. The spectacular way that the Euro has failed has led some to wonder why the Euro came together in the first place. Interestingly, the concern about a systemic Eurozone crisis did not really register when the Greek crisis started. Perhaps there were some musing that the crisis could spread, but it seemed that people believed that there would be a rescue package and that the crisis would pass. Size would come to the rescue, and an economy larger than that of the United States, the EU would end the threat of financial contagion.

If only they were right.

We now see massive capital outflows from the periphery and a frighteningly fast flight to quality within the Eurozone. But wasn't size supposed to blunt these impacts? While this narrative of "strength through fragility" seems hopelessly naive now, it's interesting to note that even in May/June of 2010 Foreign Affairs published an article by Richard Rosecrance extolling the benefits of size and larger currency arrangements in an era of turbulent capital flows. Rereading the essay, many of the key passages remind us how hindsight is 20-20, and that the narrative of the day can often push policy in the wrong direction.

The essay starts with a description of the Asian financial crises that roiled markets in the late 1990's as a justification for larger economic zones and currency areas;

But eventually the trading-state model ran into unexpected problems. Japanese growth stalled during the 1990s as U.S. growth and productivity surged. Many trading states were rocked by the Asian financial crisis of 1997-98, during which international investors took their money and went home. Because Indonesia, Malaysia, Thailand, and other relatively small countries did not have enough foreign capital to withstand the shock, they had to go into receivership. As Alan Greenspan, then the U.S. Federal Reserve chair, put it in 1999, "East Asia had no spare tires." Governments there devalued their currencies and adopted high interest rates to survive, and they did not regain their former glory afterward.

Russia, meanwhile, fell afoul of its creditors. And when Moscow could not pay back its loans, Russian government bonds went down the drain. Russia's problem was that although its territory was vast, its economy was small. China, India, and even Japan, on the other hand, had plenty of access to cash and so their economies remained steady. The U.S. market scarcely rippled. 

Small trading states failed because the assumptions on which they operated did not hold. To succeed, they needed an open international economy into which they could sell easily and from which they could borrow easily. But when trouble hit, the large markets of the developed world were not sufficiently open to absorb the trading states' goods. The beleaguered victims in 1998 could not redeem their positions by quick sales abroad, nor could they borrow on easy terms. Rather, they had to kneel at the altar of international finance and accept dictation from the International Monetary Fund, which imposed onerous conditions on its help. In the aftermath of the crisis, the small trading states vowed never to put themselves in a similar position again, and so they increased their access to foreign exchange through exports. Lately, they have proposed forming regional trade groups to get larger economically, by negotiating a preferential tariff zone in which to sell their goods and perhaps a currency zone in which to borrow cash.

The story of the Eurozone has shown us that increasing lending through large currency zones is a fool's errand. Private capital flows to the periphery destroyed the PIGS' competitiveness even though they were being quite fiscally conservative. The ability to borrow easily in good times has actually worsened situations, as at the first sign of debt troubles capital can quickly move out of the periphery states, again forcing them to "kneel at the altar of international finance and accept dictation from the International Monetary Fund."  Comically, Greece has been forced to accept "onerous conditions" from Germany for their help in the bailouts. While larger currency areas may secure cheaper funding in the short run, long run capital prospects don't improve.  In a sense, the larger currency area is a form of manufactured stability. It allows volatility to be restrained in good times, only to become fearsome in times of crisis. It's a blowup strategy, with all of the risk in the far left tail.

Moreover, a common currency area actually prevents a country from pursuing the other solution to sudden financial shocks: higher trade in goods. While the Asian economies faced the problem that "the large markets of the developed world were not sufficiently open to absorb the trading states' goods", Greece is facing the exact opposite problem. Past capital flows have left the economy severely overvalued and other markets may be willing to buy Greek goods, if only Greece could devalue its currency!  To worsen the problem, fears about Greece's debt situation lowers global equity values, further reducing global demand for Greek goods! A common currency area takes away from the external devaluation adjustment mechanism and therefore only aggravates the problems that small countries face in financial crises.

The fact that there is no adjustment mechanism makes the larger market available to each state less useful.  According to Rosencrance:

The 27 states that now compose the European Union will soon be accompanied by almost ten others, making Europe stretch from the Atlantic to the Caucasus. Member states have benefited from participating in an enlarged market extending beyond their national borders. The absence of tariffs in the EU allows greater cross- border commercial cooperation, which promotes specialization and efficiency and provides consumers in the member states with cheaper goods for purchase. Over time, as economists such as Andrew Rose and Jeffrey Frankel have shown, such trade zones increase their members' trade volume and GDP growth. There are also administrative advantages: southern and eastern European states with less advanced economies have found help and tutelage from veteran EU members and have not been allowed to fail (even if their fiscal policies have been reined in). 

But when Germany is running a massive current account surplus vis-a-vis virtually every other member of the Eurozone, the cross-border commercial cooperation is a joke. The cheaper German goods for purpose are only that way because of past capital flows that rendered periphery states uncompetitive. Given the horrendous costs of internal devaluation and the low likelihood that it would restore problems with capital structure, any possible microeconomic efficiency from trade is being swamped by disastrous levels of youth unemployment and civil unrest. When there's no transfer union, these asymmetric effects of trade flows on the different countries become incredibly important. We can no longer say "Europe is benefiting", we instead see the periphery on the verge of a full-fledged financial contagion.

It is also interesting to note that Rosencrance also makes an institutional argument here. Through the interaction of the "responsible" core states with the "irresponsible" periphery states, the periphery states will be brought up to the core states' level of institutional maturity. However, large capital flows promoted by a common currency rendered these kinds of supply side reforms unnecessary, and this institutional shift never happened. And as the Eurozone drama is unfolding, it is quite possible that some periphery states will be allowed to fail as the "help and tutelage from veteran EU members" abandons them.

In addition to these new harsh economic realities, the political realities of the situation don't seem to match up with Rosencrance's arguments either. According to the essay:

The peaceful expansion of trade blocs today, moreover, is likely to bring outsiders in rather than keep them out. It has done so in Europe and to some degree in North America and Asia as well. Self-sufficient trade blocs are impossible and will not be sought after. The key to a successful trade group, in fact, is that as it grows, it attracts sellers from the outside. 

What would China, India, and Japan do if the United States and the EU formed a trade partnership? They would not find an Asian pact a satisfactory rejoinder to the transatlantic combination. Since the major markets of the world are located in Europe and North America, Asian exporting nations would have to continue to sell to them. And if Japan eventually joined the partnership, the stakes for China and India would rise. China and India might not be significantly challenged if they could substitute domestic sales for exports. But even they, as big as they are, could not do so entirely. However important Chinese consumption becomes, it will not be able to sop up all the goods that China currently exports to technologically advanced and luxury markets in Europe, the United States, and Japan. To avoid falling behind, Beijing and New Delhi would need a continuing association with markets elsewhere.

What all this means is that the patterns of global politics and economics that have prevailed for the last half millennium are increasingly outmoded. During that period, eight out of the 11 instances of a new great power's rise led to a "hegemonic war." With a potential Chinese challenge looming in the 2020s, the odds would seem stacked in favor of conflict once again, and in other eras it would have made sense to bet on it. 

Yet military conflict is not likely to occur this time around, because even if political power sometimes repels, today economic power attracts. The United States does not need to fight rising challengers such as China or India or even to balance one off against another. It can use its own market capacity, combined with that of Europe, to draw surging protocapitalist states into its web. 

During the Cold War, the economic force of the West eventually surpassed and subverted even the heavy industrial growth of the Soviet economy. In the 1980s, the attractions of North Atlantic, Japanese, and even South Korean capitalism were a critical factor in Soviet leader Mikhail Gorbachev's decision to renew his country's economic and political system -- and end the Cold War. They also helped stimulate Deng Xiaoping's reforms in China after 1978.
Now that the formula for capitalist economic success has become widely understood and been replicated, Western economic magnetism will stem not just from the triumphs of individual economies but from their development as an increasingly integrated group. The expansion and agglomeration of economies in Europe -- and perhaps also across the Atlantic -- will serve as a beacon for isolated successes such as those in Asia.

The argument here seems to be that larger scale economic integration would be a virtuous cycle and therefore serve to moderate international conflict. As a result of integration in certain regions, other states will be forced to integrate with them, creating a unified global trade and financial regime. However, if large economic regime are important to this process, doesn't this just heighten the fragility embedded in the international system? This is especially vivid for the Euro now as, if anything, the collapse of the Eurozone would severely discredit the argument that open, integrated, western economies are the correct way forward. The failure of agglomeration would serve as a deterrent to the "isolated successes such as those in Asia."

A look back on such essays about the Euro serves as a reminder on how limited our capacity for prediction really is, and how the common narrative at any given time can hide the fragilities that persist in complex systems. The reasons behind large scale political developments such as the EMU are often opaque and create economic regimes whose faults are only revealed to us later. It is for this reason that our awareness of fundamental fragilities within economies is incredibly important. We do not know what we are truly doing, so we must strive towards a system that is robust to our errors.

Chinese Economic Slowdown: A Reminder of How Little We Know

For an economy of such complexity and opacity, how could one not be afraid?

With most of the developed world stuck in economic doldrums and the Eurozone quickly falling apart, developing countries like China have been a key source of economic growth. However, with recent developments in China, there's a substantial fear that Chinese growth could suffer a hard landing and go through the worse period of the crisis-inducing lower rate of less than 7%.

I won't offer any predictions on how this will evolve, but rather I find that this is another instance of how fragility and uncertainty are incredibly important to the evaluation of a macroeconomics. Much like my analysis on Chinese housing, the real concern is how far the left tail can go and how little we know about it. In addition to what we think we know, we need to be very aware of possible domains that contain unknown unknowns. One key area is the extent of the feedback mechanisms that link the economy together. Once we know about them, they seem obvious. The problem is trying to figure them out.

Housing is rapidly unwinding, inventories are overflowing, and government is unlikely to respond due to credibility issues. The slowdown in housing and shipbuilding is disrupting steel production. Internationally, steel production may cause Australia to slow down substantially. Domestically, the drop in steel production reduces electricity demand. Lower electricity demand causes defaults on coal contracts. Weakening export production means problems in providing Yuan liquidity as capital takes its flight to quality away from China. And on top of these interactions, shadow financial firms are folding as a result of the unsustainability of their ponzi schemes. The shadow banking internal link is particularly fearsome because there's a risk of a major systemic crisis. From Chovanec:

The concern in China is that — like that tornado — a drop in the local property market, or a decline in exports, could hit all borrowers at once, overwhelming the local credit guarantee company and leaving the banks high and dry. The risk is exacerbated by the fact that many credit guarantee companies were capitalized with loans from the same banks whose other loans they are guaranteeing. In effect, banks are insuring themselves, or each other, and would still end up holding the bag on loan losses that are supposedly insured. (It would be interesting to know how such “guaranteed” loans are treated when regulators perform their much-vaunted stress tests on Chinese banks. I suspect these loans are considered loss-proof, because they are “insured.”)

None of these connections are meant to be predictive; rather they are meant to show the limited capacity of prediction. These linkages were not publicized in earlier articles; now that they are uncovered they can leave the domain of unknown unknowns into that of known unknowns. The quote also hints at how common regulatory approaches such as stress tests fail spectacularly in these unclear, systemic conditions. How much risk is captured in these loans, when the webs connecting the official and shadow banks are so complex? Moreover, even if one knew about these connections, shoddy paperwork would prevent understanding of the magnitude of these connections. The holes in the data also should lead one to weight the possibility of a crisis more as the risk of substantially worse data is asymmetric. There's only a slight risk that the data overstates the crisis, whereas the magnitude of an understatement is unknown and can be extremely high.

This opacity in the Chinese economy leaves me unconvinced of arguments that the strength of the government is enough to prevent any crisis from spreading. To reverse the extent of the slowdown, there would need to be a massive reform in banking, corporate governance, and the structure of government in the society. For as much as the Chinese are a pragmatic lot that are willing to pursue institutional reform, the short-term does not look good. Investment has fallen too much, and consumption spending has fallen as well. Attempts at unwinding commodity bubbles such as those in copper may result in large unknown impacts as a result of copper's role in financing deals. Monetary policy also seems very uncertain given the complex network of repo/RRR cuts/interest rate controls/loan to deposit ratio requirements that severely distort what one would consider regular open market operations for easing. Capital flows look to be partially reversing on count of lower exports and higher demand for oil. Local currency loans grew nearly 300% from 2008 to 2009, adding massively to fragility. And fuhghedabout optimal hedging; how do we even know the probabilities? The way housing is unwinding rapidly is also concerning for the possibility of government intervention as land transactions have been an important source of revenue for governments. The Telegraph UK article has an important warning against the thought that government firewalls could stop a crisis:

The property correction is deemed benign because it is planned. Premier Wen Jiabao wishes to forces down prices as a social welfare policy. Yet did the Fed not slam on the brakes in 1928 to choke an asset boom? Did the Bank of Japan not do likewise in 1990, only to find that boom-bust deflation has its own fiendish momentum? Once you let credit rise by 100pc of GDP in five years – as China has, more than in those US or Japanese episodes – you are at the mercy of powerful forces.
Something odd is now happening. The People's Bank said new loans fell from $160bn (£99.5bn) in March to $108bn in April. Non-conventional lending seized up altogether. Trust lending fell by 96pc, bankers' acceptance bills by 90pc. This is astonishing data.
It may not be as easy for Beijing to turn the tap back on again. Loan demand has been falling for months. Banks are offering credit. Companies are refusing to take it. This is the old Japanese story of pushing on a string, or the European story today.

Given the precarious financial system, it's not unthinkable that there can be self-fulfilling prophecies that can overwhelm any kind of government stopgap. Once government measures start to fail, confidence in subsequent policies evaporates, the music stops and market participants scramble for chairs. Beware manufactured stability. Just because it's coming from a developing country doesn't make the warning any less true.

P.S. This gloominess hides an asymmetry in my personal bets on Chinese growth. In any given month, China is likely to grow at a moderate risk with a non-negligible chance of a catastrophic decline.  There isn't some convex set of possibilities: either it's medium good or really bad.  With all the possible feedback loops, a small  but critical perturbation is likely to cause much larger impacts.

Complexity in Monetary Policy: The Mechanisms Do Matter

Beware manufactured stability: "expectations management" is inherently fragile

A recent working paper adds to the discussion on monetary policy and growth.  In the model, monetary policy affects growth by allowing credit-intensive industries to engage in larger projects.  It does so by easing liquidity needs and, thus, giving firms the "breathing space" to invest.  The paper finds evidence for this by looking at industry-level financial constraint variables as well as the performance of the industry in response to monetary policy.  The key finding is that countercyclical monetary policy can have a significant positive impact on long run productivity growth, especially for recessions.  This result is robust to controlling for:

...the interaction between these measures of financial constraints and country-level economic variables such as inflation, financial development, and the size of government which are likely to affect the country’s ability to pursue more countercyclical macroeconomic policies (5).

Moreover, the regressions shed light on another unique internal link from monetary policy to growth: countercyclical monetary policy promotes higher levels of R+D spending.  While the model explains it through liquidity needs, the concept of "signal-processing" causing firms to invest inefficiently likely applies.  This suggests that if we really are in a "great stagnation" of growth and innovation, a stable nominal economy vis-a-vis monetary policy will be increasingly important.

To extend the model, if monetary policy exerts a diverse range of effects on what is considered money through safe asset creation, the effect of countercyclical monetary policy is probably stronger than what the interest rate would state.  By increasing liquidity through countercyclical policy, this allows firms to invest more:

The intuition for this proposition is simple. Firms need to hoard liquidity in order to weather liquidity shocks if the aggregate state is bad. This liquidity hoarding is costly...because of the lack of commitment of consumers. Reducing interest rates in bad times lowers the amount of hoarded liquidity, by increasing the ability of firms to leverage their net worth. This effect is weaker when the aggregate state is good because in that state, short-term profits are enough to cover reinvestment needs so that no liquidity needs to be hoarded to weather liquidity shocks that occur in that aggregate state of the world. Hence a higher marginal benefit of reducing interest rates in bad times relative to good times. This effect is strong enough to overcome a countervailing effect arising from the fact that lowering interest rates in bad times leads to an implicit subsidy from consumers to entrepreneurs, explaining that optimal interest rate policy is countercyclical (14, my emphasis).

And now the Nassim Nicholas Taleb homonculus starts screaming into my ear.

To what extent does this mechanism of monetary policy just create more interlocking fragilities?  I've previously argued that one of the problems with NGDP targeting is that it hides the complexity of the ecology of markets.  In this model, the world is encouraged to increase complexity because of a monetary regime that promotes more stable growth.  The firms with "unshakeable" expectations can leverage themselves to the hilt to try to maximize future growth.  But finance is fragile; what would happen if an unseen risk arises?  More seriously, although a debt crisis would wipe these firms out, nobody would be able to tell in the short run while those debt instruments are still there.

The issue here is that "average growth" is nowhere near as important as "variant growth".  While growth is stable most of the time, the impact of tail events rises as markets become more interconnected.  Leverage is inherently dangerous in an interconnected world because it enables complex cascading effects that go beyond the ability of our models to predict.  This uncertainty is heightened by the fact that even slight miscalibration errors can cause monstruous miscalculations.  These problems aren't solved by monetary policy either because financial crises are aggregate supply problems.  If credit mediation crashes, resources are no longer allocated efficiently, raising unit costs for all factors of production.

A possible way to get around this is if we can commit to more equity instead of leverage.  From Taleb's 10 principles for a Black-Swan free society:

5. Counter-balance complexity with simplicity. Complexity from globalisation and highly networked economic life needs to be countered by simplicity in financial products. The complex economy is already a form of leverage: the leverage of efficiency. Such systems survive thanks to slack and redundancy; adding debt produces wild and dangerous gyrations and leaves no room for error. Capitalism cannot avoid fads and bubbles: equity bubbles (as in 2000) have proved to be mild; debt bubbles are vicious.

This way, the net worth of companies can be converted into equity stakes, and funding can be obtained this way.  Given the outsize role of large events, this shift to a more black swan free society should occur before the adoption of monetary policies that could increase fragility.  In this world, monetary policy would allow for increased efficiency of markets while also preventing Black Swans from coming to roost.

Friday Links and Thoughts

 It blows my mind how large JP Morgan is.

The total notional exposure of all of JP Morgan's trades has been estimated to be $79 trillion. That's "trillion", with a "T", from a company with an equity value of $140 billion, and falling quickly.

This seems to completely go against the Rodrik argument that markets need to be embedded into governance.  To give an idea of scale, global GDP is only around $63 trillion.  Similarly, Spanish banks are dealing with borrowing levels of  €1.15tr, while Spanish GDP is only €1.41tr.  The top 5 banks in the United States have combined assets of $8.5tr, which is over half of U.S. GDP.  How is this sustainable or, more importantly, not fragile?  Natural environments that have endured for millennia never have animals big enough to crash the entire ecosystem.  Why are we allowing our economies to have gigantic banks?

The republicans are roughing up military alternative energy policy again.  Those ruffians.  It looks like allowing the military to do what they want is alright when it involves large amounts of fossil fuels, but becomes something legislation needs to solve when it starts involving biofuels.

More Greek debt/capital flow drama.  Politics is twisting itself into unsolvable knots; capital is slowly hissing out of the country.  It seems peculiar how this could be controlled further.

Puerto Rico's debt is quite fragile, although I don't know if I would call it a "black swan" like this article.  It frustrates me how often "Black Swan" is used to describe low probability, high impact events.  To me, Taleb's formulation of the Black Swan is an epistemological one.  It's an argument that we don't know what happens at low probabilities, and that these low probabilities are the ones that carry the greatest historical significance.  For day to day affairs, I think more often of fragility and tail risk.

All the money is flowing from peripheral Euro countries and flying into Germany and the United  States.  This is interesting, as it reflects grave fears about the ability of the peripheral Euro countries to pay back their debts.  However, what does this say about perceptions of Germany in the event of a Euro breakup?  Considering the astronomical, unknown costs of a Euro breakup, shouldn't the bond yield for Germany be higher?  After all, they are the ones who will have to bear the weight for most of the lost debt.

David Beckworth brings up an interesting link between indebtedness and expectations of nominal income growth.  While it indicates NGDP targeting may help circumvent a balance sheet recession, it also indicates that NGDP targeting results in financial fragility.  This connection is intriguing because artificial stability in one domain (NGDP) creates fragility in another (debt).  This contrasts with Evan Soltas' recent look at how NGDP targeting helps robust business growth.  If the structural signals are not confused for the macroeconomic noise, markets allocate resources better.  In this other domain, stability in NGDP actually creates more dynamism among businesses.  This is an important general problem for the analysis of fragility.  Does fragility in one domain promote the same in others, or does stability in one domain promote antifragility in others?

Capital flows: the umbrella you have to return when it rains.  BRICs are all suffering capital flight; bad news for the stability of finance.

Grexit Opacity: Why Are We Predicting Again?

Do we know how much we don't know?

(Photo credit from Reuters)

With the recent Greek elections, there's been a lot of talk about a possible Greek exit (or the cutely named "Grexit") from the Euro.  What has struck me about the situation is how much of it is still "up in the air".  You know, with  €436M dollar bonds lying around to be paid and high levels of uncertainty on what the EUR/USD exchange rate will end up as.  Much of it will be dependent on the political resolution in Greece, but also on political resolve in core countries such as Germany and France.

But time is running out.  If the breakup is going to work, it needs to be a surprise, but with European integration as is it's hard to imagine how Greece would be able to prevent capital flight.  Moreover, if they decide to break from the Euro, the other periphery countries would have a great incentive to leave the Euro as well, leaving only Germany to deal with the loss of so much Euro denominated debt.  Immediate losses could easily reach 400 billion in initial bank losses.

To me, this all illustrates how little we truly know about the way highly interconnected and leveraged economies work.  How do you build a model in which economic conditions in Greece endogenously create the political conditions over many rounds, endogenously weakening the political situation in Germany to create a contagious financial crisis over Europe?  There is no analytically tractable way to solve that system!  Moreover, we know about these factors now that there's been so much turmoil, but how was one supposed to be able to forecast all these issues beforehand?  I also see this as a worrying issue about NGDP targeting.  While, in the long run, NGDPLT makes sense, the real question is how the credibility is established in the short run.  If unanchored expectations can have so much impact on the interpretation of debt, it seems terrifying that so much of the global economy would become pinned on the monetary decisions of a few.  It just creates a whole new host of unknown possibilities.  We can barely work with the unknowns that we know about; I dread to think of the unknown unknowns that still remain.

The presence of those unknowns is also asymmetric.  There is likely nothing left that will manage to make Greece substantially better.  Had there been a quick fix, it would have already been tried.  Even if Germany decides to massively shift to increase Eurozone NGDP (which would also make Germany overheat by a large amount), it wouldn't be a cure-all; serious debt and supply-side issues would still remain.  We're reasonably certain of how good it can get; we have no idea how bad the left tail can go.

We can't continue down the path of development like this.  Antifragile solutions need to be found.

VaR She Blows!

It would be VaR-y funny if it weren't for the fact it's so VaRy dangerous

How do we know if an asset is risky, and how do we measure it?  One of the most common metrics is something called VaR, or value at risk.  From RiskMetrics:

VaR (Value-at-Risk) is the loss in a future period associated with a given quantile or confidence interval. For example, if there is a 5% chance our portfolio will lose more than USD 1mm over the next day, then we would say the one day 5% quantile (or 95% confidence) VaR is USD 1mm.

At first glance, this measure might seem reasonable.  The most common confidence level for VaR is actually 99%, which could naturally lead one to wonder why that wouldn't be enough.  But look at the definition carefully.  VaR only tells you the probability that the cost is more than a certain number; it gives you no idea how much money could be loss.  Of course, there's a wide variety of criticisms of VaR based on it's Gaussian methodology, but what I want to discuss here is how VaR functionally ignores high impact events!  VaR tells you nothing about the nature of a portfolio because everything in the tails becomes opaque and unknowable.  This creates an incentive to collect pennies on the train track because the impact of the train isn't in the confidence interval.  We get blowup strategies, like the one shown below:

VaR breaks down in a particularly tragic manner when the market under stress.  Systemic crises are rare, but due to the nature of our financial system, they're definitely something we need to worry about.  As an alternative, the Basel commission is considering an old alternative: Expected Shortfall (ES).  The difference, again from RiskMetrics:

VaR as a measure of the quantile of the P&L distribution has a history that extends back to at least the 1980s. The publication of the RiskMetrics Technical Document in 1994 established VaR's dominance over standard deviation as a measure of portfolio risk, particularly for portfolios with optionality. 

Expected shortfall incorporates more information than VaR. VaR tells you the loss at a particular quantile q. It therefore tells you nothing about what the distribution looks like below q. Expected shortfall gives the average loss in the tail below q. 

This is particularly important for portfolios that are short optionality. For such portfolios, as the market falls, losses accelerate. So VaR may look mild, but the average loss given that at least VaR is lost may be very large. 

Another major reason for preferring expected shortfall to VaR has to do with portfolio optimization. Portfolios with optimal VaR often exploit a VaR defect: its lack of subadditivity. In effect, an optimal VaR portfolio is likely to find a low VaR, high expected shortfall portfolio.

In short, VaR tells you the risk that you lose big.  ES tells you how much you are likely to lose if you lose big.

This is definitely an improvement, but how the designers know what the ES is?  How would they even calculate it?  The methodology behind the metrics are still eerily similar: they both rely on a normal world and reasonably predictable events.  But even in that world, a slight miscalibration in your standard deviation can create huge gaps in your perceptions of risk.  Considering how sensitive probability calculations are in the tails, why do we even try?  In the testimony of Richard Bookstaber, he is optimistic about the future of metrics like VaR:

I remember a cartoon that showed a man sitting behind a desk with a name plate that read ‘Risk Manager’. The man sitting in front of the desk said, “Be careful? That’s all you can tell me, is to be careful?” Stopping with the observation that extreme events can occur in the markets and redrawing the distribution accordingly is about as useful as saying “be careful.”  A better approach is to accept the limitations of VaR, and then try to understand the nature of the extreme events, the market crises where VaR fails. If we understand the dynamics of market crisis, we may be able to improve risk management to make it work when it is of the greatest importance.      

Perhaps you may one day be able to understand those dynamics, but given the fragility of your tools, wouldn't it be better to create robustness?  Small probabilities are near impossible in Gaussian worlds, much less worlds shaped by low probability, high impact Black Swans.

Take-the-Best Statistical Model

Why do we do multiple regression?

Multiple regression is the workhorse of econometrics.  Almost every empirical paper in economics relies on it, and it also forms the basis for a large majority of political science research.  But is this a valid model for prediction?  How sensitive are the results?

As it turns out, the answer is "very".  Multiple regression is sensitive to a host of issues, including normality, linearity, and low error data.  But if the real world doesn't always fit these assumptions, why do we try to use the model to predict the real world?  One thing to notice when reading the empirical papers is that they often tell you that a certain coefficient is statistically significant, but rarely does one see a given confidence interval for that coefficient.  Of course, listing confidence intervals for every coefficient, especially when there are so many, is quite cumbersome.  Yet this convenient omission often leads us to be too confident about our estimates.  How much do we really know?

Behavioral economists have long criticized this model of human decision making because there's no feasible way that we can run a regression in our head and then make a decision.  At least, I know I don't.   Although many of my friends may have used excel spreadsheets to decide where to go to college, they did not end up basing their decision on some kind of complex regression model.  It's just too computationally intractable for everyday use.

Furthermore, one key problem of multiple regression is ecological validity.  We know that the regression model predicts the sample pretty well, but does it predict the future with any accuracy?  Especially if the future is highly variable and uncertain, why should we trust our Gaussian methods that are highly sensitive to outliers? According to Gerd Gigerenzer, the most accurate rules are often not the high powered intensive statistics methods.  Rather, fast and frugal algorithms that actually limit the information they evaluate can create more accurate results.

One of the prototypical fast and frugal algorithms Gigerenzer describes is Take the Best.  While multiple regression would look at all the data and perform various tests on individual data's contribution to the dependent variable, Take the Best does a sequential evaluation of a list of key determinants.  For example, multiple regression would decide between two restaurants by looking at all the data: food quality, wait time, location, parking spots.  It would then weight each input carefully according to an equation, and then look at the results of the equations for the two restaurants.  Whichever restaurant returns the higher value is the restaurant that's chosen.  On the other hand, Take the Best would look at whether the food quality gap is high.  If so, then pick the restaurant with better food.  If the gap is not high enough, move on to the next rule and repeat this simple process.  Computationally, this would require M+1 evaluations, which is in linear time and computationally quite tractable.

Gigerenzer applied this heuristic to predicting Chicago high school dropout rates.  Given two high schools and all the associated statistics: attendance rate, proportion of low income students, social science test scores, and more, what's the most accurate way to predict which high school had a higher dropout rate?   Gigerenzer and his fellow researchers took half the population of schools and built a Take the Best model and a multiple regression model to explain the data.  No surprise, multiple regression did better, predicting over 70% of the pairs correctly while Take the Best only managed around 65%.  Yet when the two models were tested on the other half of the population, Take the Best had about a 60% accuracy rate and multiple regression barely manged around the low 50%'s.

Surprising?  Gigerenzer in Gut Feelings explains:

But why did ignoring information pay in this case? High school dropout rates are highly unpredictable-in only 60 percent of the cases could the better strategy correctly predict which school had the higher rate, (Note that 50 percent would be chance.) Just as a financial adviser can produce a respectable explanation for yesterday's stock results, the complex strategy can weigh its many reasons so that the resulting equation fits well with what we already know. Yet, as Figure 5-2 clearly shows, in an uncertain world, a complex strategy can fail exactly because it explains too much in hindsight. Only part of the information is valuable for the future, and the art of intuition is to focus on that part and ignore the rest. A simple rule that relies only on the best clue has a good chance of hitting on that useful piece of information.

Looking backwards can hurt; you might end up blindsided by what the future can hold. The data might show trends that are only valid for the sample, and not the population as a whole.  Your results won't be ecologically valid if all the data is taken into consideration.  Especially since correlations change substantially over time, Gaussian methods are more likely to offer the pretense of knowledge than knowledge itself.

This has massive policy implications.  From Gigerenzer:

According to the complex strategy, the best predictors for a high dropout rate were the school`s percentage of Hispanic students, students with limited English, and black students-in that order. In contrast, Take the Best ranked attendance rate first, then writing score, then social science test score. On the basis of the complex analysis, a policy maker might recommend helping minorities to assimilate and supporting the English as a second language program. The simpler and better approach instead suggests that a policy maker should focus on getting students to attend class and teaching them the basics more thoroughly. Policy, not just accuracy, is at stake.

Yet with these policy issues at stake, it's surprising that fast and frugal algorithms aren't used more in economics research.  One disadvantage of take the best is that it doesn't give much quantitative accuracy.  It only tells which value is higher, but not by how much.  But how much does that matter?  While multiple regression may give more statistically significant coefficients, do we really have the power to tune the economy that much?  Even the best of natural experiments don't result in parameters that don't change through time.  Romer and Romer beautifully estimate tax elasticity, but how arrogant would a person need to be to build our entire tax policy based on an estimation from an almost 80 year old data set?  DSGE quantitative accuracy is such a joke that peripheral ad-hoc models are needed to make them even somewhat useful.

These alternative statistical tools are likely to add much to our insight of models.  The development of robust heuristics will be critical in a complex world, in which calculation becomes increasingly difficult and Gaussian methods increasingly fragile.

99 Reasons to Fail: Financial Monoculture

Is size necessarily fragile?  Another look at Too Big to Fail

The scale of finance has drawn heightened scrutiny in the years after the financial crisis.  Yet in spite of this concern, the size of banks has only grown.  There's fear that the government may have to intervene again if another financial crisis comes along, and Fisher, the Dallas Fed president, has blasted this trend.

It is imperative that we end TBTF. In my view, downsizing the behemoths over time into institutions that can be prudently managed and regulated across borders is the appropriate policy response. Only then can the process of “creative destruction”— which America has perfected and practiced with such effectiveness that it led our country to unprecedented economic achievement— work its wonders in the financial sector, just as it does elsewhere in our economy. Only then will we have a financial system fit and proper for serving as the lubricant for an economy as dynamic as that of the United States.

Of course, the regulatory confusion that would arise from breaking up the banks would have massive effects, but are there also other theoretical reasons to be suspicious of "just" breaking up banks?  According to the traditional narrative, large banks are too vulnerable to unseen risk.  If all the models are calibrated, and something outside those models surfaces, the entire machine could break down.  This puts the entire market in jeopardy.  Large banks, knowing this, are then willing to take on more risk, as they know that the government will intervene to save them.  This combination of factors then creates a "too big to fail" phenomenon, and society pays the price while the bankers continue picking up pennies off of the train tracks.

Yet small banks are very vulnerable as well.  What sometimes can be forgotten from discussions of banking is that big banks is why they are so problematic.  Systemic risk is at the root of the problem.  With their high frequency Gaussian models to hedge alphas, betas, deltas, gammas, a single tail even can cause a the largest bank to collapse, sending ripples through the entire financial system.  An analysis of the Fed's interbank lending system showed that 75% of the payments involved 0.1% of the nodes and 0.3% of the linkages between nodes in the banking network.  From this image, it's very easy to imagine an explosion causing one bank causing a cascade throughout the network.

But is size the only issue?  Not necessarily.  Some earlier studies about bank resiliency actually indicated that larger banks should actually decrease bank failure!  Bank, as a result of their size, are able to diversify more and limit their exposure to sector-specific volatility.  Notably, the United States actually has relatively low bank concentration compared to other countries.  The three largest banks in the United States only controlled 19% of the industry in 2003, while the corresponding numbers for Finland and New Zealand were 85% and 77% respectively.  Research from the NBER found that a one standard deviation increase in bank size resulted in about a 1 percentage point decrease in bank failure proportions.  Considering the percentage risk of bank failure was only 4% in the whole sample, the 1 percentage point would have been a significant decrease in bank failure rates.

Of course, this does not suggest that large banks were better; the fact that many of them collapsed in the recent financial crisis suggests that this isn't the case.  Additionally, the focus on the probability of bank failure glosses over the issue of magnitude; the smaller number of bank failures most likely had massive effects.  But this does suggest that the relationship is not as simple as one might think, and that the true relationship likely has a severe non-linearity.

Moreover, a market populated by small banks is prone to a crisis because there's "too many to fail".  If the market is fed on the same monoculture of debt priced with bad models, there's still the risk that a systemic crisis could run through the system.  With VaR models that are ill suited for complex environments and hyperspeed algorithmic trading models, it's not unthinkable that traders could feed on themselves and trigger stock market shocks.  This system would be difficult, if not impossible to effectively monitor, especially when any given bank can have large systemic effects.

As a result, some have called for an increase in the diversity in financial systems.  The voxEU article specifically outlines four positive externalities from diversity:

1. Bailout/Moral Hazard Externality - banks tend to pursue the same investment as they are consequently more likely to be all bailed out.
2. Systemic Risk Externality - as banks have a hard time taking into account the effect of its actions on other firms, this leads to inefficient levels of systemic risk with homogenity
3. Herding/Momentum Externality - as markets tend to herd, whether for psychological or principal-agent reasons, increasing diversity would limit the swings in the market.
4. Insurance Externality - higher diversity makes cross-insurance more robust, reducing risk

With the discussion framed in terms of externalities, the natural answer is a pigouvian tax.  The authors propose a system of capital requirements based on how much a given bank's profits or share prices correlate with the market as a whole.  The government would "tax" banks who "go with the flow".  This would take into account both "too big to fail" as well as "too many to fail".  Large banks would be required to hold more capital as they, by virtue of their size, are highly correlated with the market.  Small banks would be also pushed to try different strategies to avoid higher capital requirements.  The simplicity in the rule is also incredibly elegant; a heuristic, and not a model error sensitive parameter.  Thus, in an inverse of Taleb's criticism of the current financial system, this kind of macroprudential regulation may promote a certain level of antifragility as individual banks could play for the lottery tickets with undefined payoffs.  It may not be enough, but coupled with robust layers of monetary policy, there may yet be hope for complex economies in an unpredictable world.

Nominal GDP Targeting and Complexity

The Complexity View

I've recently started rereading passages of The Black Swan: The Impact of the Highly Improbable and I find it fascinating. The prose is fluid, and the arguments are powerful. Much of the book mocks economic theory, as models tend to minimize the role of large shocks that defy normal distributions.  In the book, Taleb inserts the following chart that shows how much these "outliers" influence the stock market.

Taleb places the blame for these large swings in the market on the shoulders of the Federal Reserve.  He argues that stabilization policy actually makes the economy more fragile, making it more likely to go down in a dramatic fashion once the "big one" hits.  He sums up this argument in the following quote from a section titled "Beware Manufactured Stability" in a supplementary essay.

...fear of volatility, leading to interference with nature to impose "regularity" makes us more fragile across so many domains.  Preventing small forest fires sets the grounds for more extreme ones; giving out antibiotics when it is not very necessary makes us more vulnerable to severe epidemics... 

Which brings me to another organism: economic life. Our aversion to variability and desire for order and our acting on it has helped precipitate severe crises... Another thing we saw in the 2008 debacle: the U.S. government (or, rather, the Federal Reserve) had been trying for years to iron out the business cycle, making us exposed to a severe disintegration. This is the sort of reasoning I have against "stabilization" policies and manufacturing a nonvolatile environment ...

In a sense, the reduction of volatility in the Great Moderation was only an illusion of stability.  We were, as Taleb would say, "sitting on a pile of dynamite," unaware of the risk that lay underneath.

Impact on NGDP Targeting Policy

This kind of critique seems rather damning against nominal GDP targeting.  The typical analysis of NGDP targeting hinges on the assertion that low volatility implies high stability.  But what if this isn't true?  What if these times of low volatility are just times of high fragility?  Some analysis of the arguments for NGDP targeting even suggest mechanisms by which this is the case.  Debt problems are waved away because NGDP is stable, financial opacity becomes a non-issue because monetary policy compartmentalizes it,  perceptions of "safe" assets  change because expectations of nominal growth are maintained.  Stable expectations permit these innovations because agents can plan ahead, allowing for higher growth.

However, this higher efficiency comes at the cost of redundancy.  Taleb jokes in an interview with Russ Roberts that:

An economist would never design a human being with two lungs and two kidneys. It's wasteful. Deadweight loss.  

He follows up with:

So, the opposite of spare parts would be debt. And nature doesn't like debt. Nature likes redundancies. This mechanism of overreaction is redundancy.

And this is what terrifies me about NGDP targeting.  The incredibly stable regime creates an environment in which redundancy is eschewed in favor of fragility.  Perhaps it would be better to have a more resilient economy that wouldn't be able to accumulate as much capital, but one that has lower levels of debt.  The cost of a mistake in an NGDP targeting world would be incredible.  Even if, theoretically, under a stable monetary regime, there are no demand-side recessions, would you be willing to bet the stability of the entire global financial system on it?  Even if it were true, can you guarantee the Fed will be able to maintain a "stable monetary regime" for perpetuity?

I'm not trying to say the current monetary system is ideal; the dismal employment numbers firmly reject that view.  But when we look onto NGDP targeting as the solution to the global economic malaise, we need to be careful that we don't put all of our eggs into one basket.  NGDP targeting is an incredible tool for monetary policy; but it can't be a panacea for all of these troubles.  

This critique of NGDP targeting brings up another key issue for the design of policy.  Optimal policy has to do more than maximize welfare, it must also be robust to errors.  While in the game playing, platonic world of models NGDP targeting should create incredible reductions in volatility and instability, what are the possible effects on global fragility?  Policy engineering needs to take into account Murphy's Law: "If anything can go wrong, it will."  The only question is how we prepare.