Tag Archives: finance

Connecting the Instability of Markets and Ecosystems – C.S. Holling and Hyman Minsky

Both markets and ecosystems can, and have, been viewed as being shaped by feedback processes that push them towards a steady state – in markets this is the “invisible hand” – in ecology it is “succession.”  However, what has been appreciated in ecology, and has been reluctantly included in economics is that these invisible hands can push systems into turbulence or even tear them apart.

The 2008 financial crisis revived widespread interest in the work of American economist Hyman Minksy who developed a theory on the evolution of financial crises that not only provides a strong framework to understand the forces that created the crisis but also has strong parallels to the work of Canadian ecologist C.S. “Buzz” Holling, an originator of resilience thinking, who developed a theory of social-ecological crises that shares many features with Minksky’s theory.

Minsky and Holling both showed how successful regulation could lead systems into a trap of decreasing resilience and increased vulnerability.

Minsky’s “Financial instability hypothesis” argues that as an economy flourishes people and organizations lose their motivation to consider the possibility of failure, because the costs of concern are high and apparent while the benefits of a relaxed attitude are immediate.  Loans become less and less secure, bad risks drive out good, and the resilience of the entire economy to shocks is reduced. Minsky argued that economic resilience is slowly eroded as there is a shift of dominance between three types of borrowers: hedge borrowers, speculative borrowers, and Ponzi borrowers.   Hedge borrower have a cash flow that they can use to repay interest and principal on a debt, while the speculative borrower can cover the interest, but must continually roll over the principal, and Ponzi borrowers, who have to borrow more to cover their interest payments.  Hedge borrowers are least vulnerable to economic changes, while Ponzi borrowers are the most.  As the economy does well, speculative and Ponzi borrowers can outperform safer borrowers.  For example, highly leveraged investments in housing can yield big profits as house prices increase, driving further investment in housing and housing price increases.  As the use of Ponzi finance expands within the finance system the financial system becomes increasingly vulnerable to any change in the perceived value of Ponzi borrowers assets can trigger a collapse that includes speculative and hedge borrowers.  When a shock or change in perception causes the networks of loans to unravel, crisis moves from the financial sector other parts of the economy.  This theory fits many aspects of the 2008 financial crisis where public and private risk regulations were relaxed, and there was a lot of speculative and Ponzi borrowing in the US housing market.  For example, financial market regulationaccounting standards were lowered, and mortgage risk assessments were abandoned.

Similarly, Holling’s “Pathology of ecosystem management” argues that the management of ecosystems to increase the production of a desired ecological services often achieve their goal by simplifying ecosystems and reducing environmental variation. For example, forest management removes undesired species and suppresses wildfire and produces more timber which leads to sawmills and jobs. While these efforts are often initially successful, over the longer term these effort can trap a system into a situation where there is:

1) a high societal dependence on continuous supply of ecological benefits and

2) a declining ability of an ecosystem to recover from and regulate environmental variation.

Holling’s adaptive cycle concept grew out of the pathology of natural resource management.

Societal dependance arises as investment follows the initial success.  The decline in ecological resilience occurs because of management’s simplification the spatial pattern, food web, and disturbance dynamics of the managed ecosystem.  Often as resilience declines, management has to increasingly invest in artificial ecological regulation to maintain ecological benefits and protect its sunk investment infrastructure.  This dynamic can trap people within a social-ecological system which is unprofitable, has low resilience, and is difficult to disengage from due to sunk cost effects.  For example, logging and forest can lead to more investment in timber mills and towns and the simplified forest, which is more vulnerable to insect outbreaks.  These continual outbreaks require investment in pest control, which decreases the profitability of the logging.  Simultaneously, it is difficult to stop logging or pest control due to the people living in the towns and the investment in the timber mills.

Holling’s pathology was originally developed in the 1980s.  Since then Holling’s ideas have been substantially developed by ecologists and others environmental scientists over the past twenty years (notably in the book Panarchy).  Researchers have tried to identify different types of social-ecological traps.  Resilience researchers have created quantitative models explore and statistical methods to detect instabilities, and expanded upon the pathology to explore the roles of leadership and agency in creating new social-ecological trajectories.

Unlike Holling’s work, Minsky’s work has been largely marginalized within mainstream economics, though it has retained a dedicated following among financial and some hetrodox economists.  The lack of a rigourous mathematical structure to Minsky’s ideas seems to have been much more of a barrier in economics, than the similar lack in Holling’s ideas was to ecology.  However, I expect that the main reason for the lack of interest was that instability was not seen as a particularly relevant idea. The financial turmoil of the last few years has shown that despite economists dreams of a great moderation due to wise regulation, regulators and markets have not been able to tame the destabilizing dynamics of global markets.  Indeed, the financial crisis of 2008 and the recession that has followed has demonstrated that many regulations likely have made this crisis worse by reducing diversity, tightening couplings, and decreasing adaptive capacity.  For example, the Euro prevented countries, like Greece or Spain, from shifting their exchange rates with other countries.

However, the crisis has provoked substantial new interest in Minksy, and now eminient mainstream economists such as Paul Krugman have now attempted to connect his work to the central core of economics (see Eggertsson & Krugman 2012  paper & a critique from hetreodox financial economist Steve Keen).

The financial, political, price turbulence since 2008 has increased interest in theories of instability, but most theory is based upon stability, or short term departures from stable points.  This undersupply of theories of instability, makes the work of Holling and Minksy more valuable.  In separate realms and identifying different mechanisms, the work of Minsky and Holling suggests instability cannot be avoided, as stability creates instability.  This understanding can be used to help navigate instability, and it highlights the value of working to create new theories to understand, analyze, and navigate social-ecological instability – something that we are working on at the Stockholm Resilience Centre.

Further readings:

Holling (many followup articles are available in Ecology & Society)

  • Holling, C.S., 1986. The resilience of terrestrial ecosystems: local surprise and global change. In: Clark, W.C., Munn, R.E. (Eds.), Sustainable Development of the Biosphere. Cambridge University Press, London, pp. 292–317.
  • Holling, C.S., Meffe, G.K., 1996. Command and control and the pathology of natural resource management. Conservation Biology 10, 328–337.
  • Gunderson, L.H. & Holling, C.S. (Eds.). 2002. Panarchy: Understanding Transformations in Human and Natural Systems. Island Press.

Minsky (lots of his publications are available on the Levy Institute’s website)

  • Minsky, H. P. (1975). John Maynard Keynes. New York, Columbia University Press.
  • Minsky, H. P. (1982). Can “it” happen again? : essays on instability and finance. Armonk, N.Y., M.E. Sharpe.
  • Minsky, H. P. (1986). Stabilizing an unstable economy, Twentieth Century Fund Report series, New Haven and London: Yale University Press.
  • Wray, L.R. 2011 Minsky Crisis in The New Palgrave Dictionary of Economics, Online Edition, 2011.  Edited by Steven N. Durlauf and Lawrence E. Blume. Palgrave.

On the web Ashwin Parameswaren has been building on Minksy and Holling’s ideas at his websites Macroeconomic resilience and All systems need a little disorder.

Resilience and Euro – diversity

On MacroEconomic Resilience ex-banker Ashwin Parameswaran draws upon Holling’s pathology of natural resource management and the work of Hyman Minsky (a connection I’ve mentioned previously and Ashwin has explored extensively – see here and here) to write about The Resilience Stability Tradeoff: Drawing Analogies between River Flood Management and Macroeconomic Management.

Ashwin Parameswaran insightfully writes:

In complex adaptive systems, stability does not equate to resilience. In fact, stability tends to breed loss of resilience and fragility or as Minsky put it, “stability is destabilising”. Although Minsky’s work has been somewhat neglected in economics, the principle of the resilience-stability tradeoff is common knowledge in ecology, especially since Buzz Holling’s pioneering work on the subject. If stability leads to fragility, then it follows that stabilisation too leads to increased system fragility. As Holling and Meffe put it in another landmark paper on the subject titled ‘Command and Control and the Pathology of Natural Resource Management’, “when the range of natural variation in a system is reduced, the system loses resilience.” Often, the goal of increased stability is synonymous with a goal of increased efficiency but “the goal of producing a maximum sustained yield may result in a more stable system of reduced resilience”.

The entire long arc of post-WW2 macroeconomic policy in the developed world can be described as a flawed exercise in macroeconomic stabilisation. But there is no better example of this principle than the Euro currency project as the below graph (from Pictet via FT Alphaville) illustrates.

Instead of a moderately volatile mix of different currencies and interest rates, we now have a mostly stable currency union prone to the occasional risk of systemic collapse. If this was all there is to it, then it is not clear that the Euro is such a bad idea. After all, simply shifting the volatility out to the tails is not by itself a bad outcome. But the resilience-stability tradeoff is more than just a simple transformation in distribution. Economic agents adapt to a prolonged period of stability in such a manner that the system cannot “withstand even modest adverse shocks”. “Normal” disturbances that were easily absorbed prior to the period of stabilisation are now sufficient to cause a catastrophic transition. Izabella Kaminska laments the fact that sovereign spreads for many Eurozone countries (vs 10Y Bunds) now exceed pre-Euro levels. But the real problem isn’t so much that spreads have blown out but that they have blown out after a prolonged period of stability.

Resilience and the Euro – networks

The New Scientist recently had an article by Debora MacKenzie on resilience and the Euro.  She writes:

… The diversity of a network’s components and the density and strength of its connections – called its connectivity – affect the system’s resilience, or resistance to change. More connections make a system more resilient: if one component fails others can fill in. But only up to a point. Go past a certain threshold and more connectivity makes the system less resilient because a single failure can cascade to every other component.

The trick is to get the balance right. “Cascades of failure may be controlled by changing the nature and strength of the links between various parts of the networks,” says Fisher. Much current research in complex systems focuses on assessing connectivity correctly to enable that. Other work aims to detect behaviour that indicates an imminent collapse.

So turning 17 separate currencies into one eurozone was a cascading failure waiting to happen?

Yes. That is why Greek debt is a crisis, even though Greece accounts for only 2.5 per cent of the eurozone’s GDP. News of its debts caused the trust that markets placed in Greek government bonds to plummet. Its creditors are mainly in the eurozone, so a Greek default is causing markets to lose confidence in other members, such as Italy – which is too big to bail out.

Could the crisis have been avoided?

Complexity theory shows what went wrong. Yaneer Bar-Yam of the New England Complex Systems Institute in Cambridge, Massachusetts, says his still-unpublished studies show that investors profited by driving down the value of Greek government bonds, triggering the crisis. And, he suspects, they have now moved on to Italy. If instead of national bonds issued by sometimes weak economies, the eurozone had one common bond backed by powerhouses such as Germany, such an attack could not have happened.

Germany rejects eurobonds. But, says Bar-Yam, complex systems such as multicellular organisms show that “if you are going to accept common risk, you have to invest in defences that extend to the weakest member”. Either that or make sure an attack on a weak member cannot spread, a technique that ant colonies have perfected: the death of a single ant has little effect on the colony as a whole. “Biology has solved this problem several ways,” says Bar-Yam.

The tragedy of a common currency

The current crisis of the Euro emphasizes some basic lessons from the study of resilience of dynamic systems. Attributes of complex systems that enhance resilience are diversity, redundancy and modularity. There is a cost of maintaining resilience. The decision to have one currency among different countries in Europe was based on a focus of efficiency. This could be reached as long as economies would grow steadily and the countries kept their budgets in check.

Unfortunately, some countries did not so. Also Germany and France have broken maximum governmental budget shortages, and no actions were taken. It sounds as if the basic principles of institutional design were not met. Meaning that there was no proper monitoring and were no proper enforcement mechanisms. Surprisingly there are not even regulations how countries may leave the EU or Euro.

By creating a tightly connected system without proper enforcement it is no surprise that the resilience of the European, and global, economy has been decreased. The budget crisis leads now to a spiral of distrust among participants in the action arena of the global financial system. It does not help either that the USA is not able to reach to any solution to their own budget problems.

If there was more modularity we could afford countries to fail. But in the tightly globalized financial system, a failure leads to a cascade of dominos falling. A short-sighted focus on efficiency has led to a costly endeavor and likely collapse of the euro. We can learn from long-lasting biological systems and the importance to develop system features that enhance resilience. Hopefully during the recovery after the pending transformation more emphasis will be given to design system properties to enhance resilience.

Seed’s global reset on tipping points and systematic risk

Seed magazine has a special issue on new approaches to interconnected and complex challenges. It also features interesting articles on TEEB and ecological economics, new modes of science, forecasting, tipping points and systematic risk.  As well as,  Carl Folke’s article on resilience, which I mentioned previously.

Economist Ian Goldin writes on On Systemic risks

Systemic risk is the underbelly of globalization and technical change. Intense integration of markets, trade, and finance has accompanied the latest tidal wave of globalization, facilitated by seismic policy shifts, like those associated with the fall of the Soviet Union, the formation of the European Union, and the opening of emerging economies. Between 1980 and 2005, global foreign-investment flows increased 18 times, and trade flows increased more than sevenfold, reflecting unprecedented integration.

… While the term “systemic risk” has historically referred mainly to collapses in finance, recent decades of globalization have created new and broader risks. There has been an exponential increase in the number of nodes and pathways through which materials, capital, information, and knowledge can be transmitted at lightning speeds and with global reach. These networks also have the potential to create and propagate risk. Interconnectedness, networks’ central property, can lead simultaneously to greater robustness and more fragility. Risk can decline as connectivity increases because as risk sharing increases, so does the number of nodes and links. This is true of financial systems, manufacturing services, intellectual property, and ecosystems. However, increased fragility is also a concern. Once a tipping point is triggered past its threshold, connectivity can amplify and spread risk instead of sharing it stably.

Looming systemic risks include pandemics, which may spread more rapidly across a densely connected world, and bio-terrorism risks, which are likely to become increasingly systemic in the 21st century. The ability to produce biological and other weapons of mass destruction is becoming more widespread, especially among non-state actors, due to technological innovation (not least with the development of DNA synthesizers). Increases in population density, urbanization, and the growth of connectivity, both physically and virtually, means that dangerous recipes and panic can be instantaneously transmitted globally. And climate change, a silent tsunami that crept up on us, presents major systemic environmental, social, and economic risks to humanity.

In an article On Early Warning Signs of tipping points ecologist George Sugihara writes:

A key phenomenon known for decades is so-called “critical slowing” as a threshold approaches. That is, a system’s dynamic response to external perturbations becomes more sluggish near tipping points. Mathematically, this property gives rise to increased inertia in the ups and downs of things like temperature or population numbers—we call this inertia “autocorrelation”—which in turn can result in larger swings, or more volatility. In some cases, it can even produce “flickering,” or rapid alternation from one stable state to another (picture a lake ricocheting back and forth between being clear and oxygenated versus algae-ridden and oxygen-starved). Another related early signaling behavior is an increase in “spatial resonance”: Pulses occurring in neighboring parts of the web become synchronized. Nearby brain cells fire in unison minutes to hours prior to an epileptic seizure, for example, and global financial markets pulse together. The autocorrelation that comes from critical slowing has been shown to be a particularly good indicator of certain geologic climate-change events, such as the greenhouse-icehouse transition that occurred 34 million years ago; the inertial effect of climate-system slowing built up gradually over millions of years, suddenly ending in a rapid shift that turned a fully lush, green planet into one with polar regions blanketed in ice.

The global financial meltdown illustrates the phenomenon of critical slowing and spatial resonance. Leading up to the crash, there was a marked increase in homogeneity among institutions, both in their revenue-generating strategies as well as in their risk-management strategies, thus increasing correlation among funds and across countries—an early warning. Indeed, with regard to risk management through diversification, it is ironic that diversification became so extreme that diversification was lost: Everyone owning part of everything creates complete homogeneity. Reducing risk by increasing portfolio diversity makes sense for each individual institution, but if everyone does it, it creates huge group or system-wide risk. Mathematically, such homogeneity leads to increased connectivity in the financial system, and the number and strength of these linkages grow as homogeneity increases. Thus, the consequence of increasing connectivity is to destabilize a generic complex system: Each institution becomes more affected by the balance sheets of neighboring institutions than by its own. The role of systemic risk monitoring, then, could simply be rapid detection and dissemination of potential imbalances, much as we allow frequent underbrush fires to burn in order to forestall catastrophic wildfires. Provided that these kinds of imbalances can be rapidly identified, maybe we will need no regulation beyond swift diffusion of information. Having frequent, small disruptions could even be the sign of a healthy, innovative financial system.

Further tactical lessons could be drawn from similarities in the structure of bank payment networks and cooperative, or “mutualistic,” networks in biology. These structures are thought to promote network growth and support more species. Consider the case of plants and their insect pollinators: Each group benefits the other, but there is competition within groups. If pollinators interact with promiscuous plants (generalists that benefit from many different insect species), the overall competition among insects and plants decreases and the system can grow very large.

Relationships of this kind are seen in financial systems too, where small specialist banks interact with large generalist banks. Interestingly, the same hierarchical structure that promotes biodiversity in plant-animal cooperative networks may increase the risk of large-scale systemic failures: Mutualism facilitates greater biodiversity, but it also creates the potential for many contingent species to go extinct, particularly if large, well-connected generalists—certain large banks, for instance—disappear. It becomes an argument for the “too big to fail” policy, in which the size of the company’s Facebook network matters more than the size of its balance sheet.

What’s driving current food prices?

New Scientist interviewed food policy researchers Maximo Torero and Joachim von Braun from IFPRI about current rise in food prices and they blame financialization of commodity markets:

Is this another crisis like the one we had in 2008?

Not quite. Maximo Torero of the International Food Policy Research Institute (IFPRI) in Washington DC notes that oil, the real driver of food prices and of the 2008 crisis, is relatively cheap, at around $75 a barrel, not over $100 as it was in 2008.

In 2008, both immediate grain prices, and the prices offered for future grain purchases in commodities markets, climbed steadily for months, whereas now they are spiking and dipping more unpredictably, which economists call volatility.

“The market fundamentals – supply and demand – do not warrant the price increases we have seen,” says Torero. Not all harvests have been bad, and after 2008 countries rebuilt grain stocks. “There are enough stocks in the US alone to cover the expected losses in Russia.”

The food riots in Mozambique were not due to world grain prices, he says, but because Mozambique devalued its currency, making imported food more expensive.

So what has been happening this year?

Markets are responding nervously to incomplete information. First there was a series of shocks: Russia’s export ban, lower maize forecasts, then, days later, a US ruling to allow more bioethanol in fuel which seemed likely to further reduce the maize – the main source of bioethanol – available for food. Meanwhile there was no reliable information about grain stocks, which is strategic information that most countries keep secret.

The result was nervous bidding and sporadically surging prices in commodity markets. And that attracted the real problem: investors wielding gargantuan sums of speculative capital and hoping to make a killing. When speculation exacerbated the price crisis of 2008, Joachim von Braun of the University of Bonn, Germany, then head of IFPRI, predicted that it would continue causing problems. “We saw that one coming and it came,” he says. “Food markets have new design flaws, with their inter-linkages to financial markets.”

Volatility also makes it harder to solve the long-term, underlying problem – inadequate food production – by making farmers and banks reluctant to invest in improved agricultural technology as they are unsure of what returns they will get. “Investment in more production alone will not solve the problem,” says von Braun. As long as extreme speculation causes constant price bubbles and crashes, either farmers will not get good enough returns to continue investing in production, or consumers will not be able to afford the food.

“Without action to curb excessive speculation, we will see further increases in these volatilities,” he says.

Environmental externalities and institutional investors

The UN Environment Programme Finance Initiative is a collaboration of between UNEP and the financial sector that aims to improve the understanding of the connections between environmental and financial performance.

A new report Environmental externalities for institutional investors from UNEPFI and the UN endorsed Principles for Responsible Investing group estimates the costs to the global economy from environmental damage, in terms of consequences for investors and company profits, by synthesizing current estimates of the consequences of climate change, resource depletion, biodiversity loss and water use.

The report proposes that:

Large institutional investors are, in effect, “Universal Owners”, as they often have highly-diversified and long-term portfolios that are representative of global capital markets. Their portfolios are inevitably exposed to growing and widespread costs from environmental damage caused by companies. They can positively influence the way business is conducted in order to reduce externalities and minimise their overall exposure to these costs. Long-term economic wellbeing and the interests of beneficiaries are at stake. Institutional investors can, and should, act collectively to reduce financial risk from environmental impacts.

And concludes that:

  • US$ 6.6 trillion was the estimated annual environmental costs from global human activity equating to 11% of global GDP in 2008.
  • The most environmentally damaging business sectors are: utilities; oil and gas producers; and industrial metals and mining. Those three accounted for almost a trillion dollars worth of environmental harm in 2008. The top 3,000 companies by market capitalisation, which represent a large proportion of global equity markets, were responsible for $ 2.15 trillion worth of environmental damage in 2008.
  • 50% of company earnings that could be at risk from environmental costs in an equity portfolio weighted according to the MSCI All Country World Index.
  • Environmental damage costs are generally higher than the cost of preventing or limiting pollution and resource depletion. The costs of addressing environmental damage after it has occurred are usually higher than the costs of preventing pollution or using natural resources in a more sustainable way.

Food security and financial markets


FAO says that Food price volatility a major threat to food security:

Concluding a day-long special meeting in Rome the experts recognized that unexpected price hikes “are a major threat to food security” and recommended further work to address their root causes.

The recommendations, put forward by the Inter-Governmental Groups (IGGs) on Grains and on Rice, came as FAO issued a report showing that international wheat prices have soared 60-80 percent since July while maize spiked about 40 percent.

The meeting said that “Global cereal supply and demand still appears sufficiently in balance”, adding, “unexpected crop failure in some major exporting countries followed by national policy responses and speculative behaviour rather than global market fundamentals have been the main factors behind the recent escalation of world prices and the prevailing high price volatility.”

Among the root causes of volatility, the meeting identified “Growing linkage with outside markets, in particular the impact of ‘financialization’ on futures markets”. Other causes were listed as insufficient information on crop supply and demand, poor market transparency, unexpected changes triggered by national food security situations, panic buying and hoarding.

The Groups therefore recommended exploring “alternative approaches to mitigating food price volatility” and “new mechanisms to enhance transparency and manage the risks associated with new sources of market volatility”.

In a recent IFPRI discussion paper, Recent Food Prices Movements: A Time Series Analysis, Bryce Cooke and Miguel Robles analyze the food price spike of 2008.  They asses multiple proposed explanations (from biofuels, oil prices, weather, trade barriers, and speculative markets) using econometric time series analysis.  They conclude that financial activity in futures markets and proxies for speculation can best explain crisis.  They write:

Results of our rolling windows Granger causality tests show the following:

(1) In the case of rice prices we find weak evidence that for few 30-month intervals between 2004 and 2007, the U.S. dollar depreciation rate has marginally Granger-caused the growth rate of rice price; and also the growth rate of real world money holdings seems to be more important in explaining the growth rate of rice prices after 2004, but this evidence is not really statistically significant.

(2) When we analyze the price of soybeans we find that, starting in mid-2005 (which implies a 30-month period ending December 2007), the growth rate in the world exports of soybeans shows evidence of Granger causing the growth rate of soybean prices.

(3) In the case of corn we find that starting in the second half of 2004 the growth rate of oil prices shows evidence of Granger causing the growth rate of corn prices, but with a negative relationship.

(4) When analyzing our speculation proxies we observe that the ratio of monthly volume to open interest in futures contracts indicates that for the case of wheat and rice, starting in 2005, it has influence in forecasting price movements.

Also we find that for the case of rice, the ratio of noncommercial long positions to total long (reportable) positions has an effect on prices, starting in 2004. When we analyze the same ratio for short positions we find additional evidence for speculation affecting the growth rate of corn and soybean prices. In the case of corn there are signs of causality between March 2004 and September 2006, and during the 30-month span from May 2005 to November 2007. In the case of soybeans we find weak evidence, in particular for the 30-month period ending February 2008.

Interestingly as the rolling samples include 2008 and 2009 data, picking the decrease of grain prices since mid 2008 and the adverse effects of the global financial crisis, the evidence of speculation activity affecting spot prices vanishes in all cases. This supports the view that during the food crisis agricultural grain markets were operating under a different regime in which speculation activity played a role in spot prices formation. The overall evidence points to the following interpretation: before and after the food crisis speculation activity had no effect on spot prices formation while during the crisis it did. This is not to say that before and after the crisis speculation was not present, it was (probably to a less extent) but didn’t granger cause spot prices.

Overall, we conclude from our time series analysis that when taking the four commodities analyzed here there is evidence that financial activity in futures markets and/or speculation in these markets can help explain the behavior of these prices in recent years. Other explanations are only partially supported for the particular case of one agricultural commodity or not supported at all. We do not claim, however, that these other explanations should be disregarded; all that we can say is that in using the variables considered in this study and the particular time series models herein, we do not find such evidence.

Frederick Kaufman wrote a Harper’s magazine in July 2010 The food bubble:
How Wall Street starved millions and got away with it
that reports on finance and the food crisis. The Harper’s version is behind a paywall, but Kaufman was interviewed on Democracy Now.

More academic takes on the food crisis and the possible future of food price volatility are in:

C. Gilbert and C. Morgan’s article Food price volatility in Proc Royal Soc (DOI: 10.1098/rstb.2010.0139 ). They conclude:

We have highlighted the extensive evidence demonstrating interconnection of financial and food commodity markets as the result of speculative activity. Nevertheless, this contention remains controversial and, until the mechanisms are better understood, the policy debate will remain confused.

and

C. Gilbert’s How to Understand High Food Prices in Journal of Agricultural Economics (DOI: 10.1111/j.1477-9552.2010.00248.x) whose abstract states:

Agricultural price booms are better explained by common factors than by market-specific factors such as supply shocks. A capital asset pricing model-type model shows why one should expect this and Granger causality analysis establishes the role of demand growth, monetary expansion and exchange rate movements in explaining price movements over the period since 1971. The demand for grains and oilseeds as biofuel feedstocks has been cited as the main cause of the price rise, but there is little direct evidence for this contention. Instead, index-based investment in agricultural futures markets is seen as the major channel through which macroeconomic and monetary factors generated the 2007–2008 food price rises.

Computer trading producing new financial dynamics?

In October 1987,  stock markets around the world crashed, with the Dow Jones droping 22%.  The causes of this crash are still unclear, but one of the suspected causes was computer automated trading.  This concern lead attempts to design mechanisms to break potential viscous cycles by creating ‘circuit breakers‘, rules that halt trading if the Dow rapidly .  However, as financial engineers innovate, new risks are emerging.   The Financial Times writes Computer-driven trading raises meltdown fears:

An explosion in trading propelled by computers is raising fears that trading platforms could be knocked out by rogue trades triggered by systems running out of control.

Trading in equities and derivatives is being driven increasingly by mathematical algorithms used in computer programs. They allow trading to take place automatically in response to market data and news, deciding when and how much to trade similar to the autopilot function in aircraft.

Analysts estimate that up to 60 per cent of trading in equity markets is driven in this way.

… Frederic Ponzo, managing partner at GreySpark Partners, a consultancy, said: “It is absolutely possible to bring an exchange to breaking point by having an ‘algo’ entering into a loop so that by sending them at such a rate the exchange can’t cope.”

Regulators say it is unclear who is monitoring traders to ensure they do not take undue risks with their algorithms.

The Securities and Exchange Commission has proposed new rules that would require brokers to establish procedures to prevent erroneous orders.

Mark van Vugt, global head of sales at RTS Realtime Systems, a trading technology company, said: “If a position is blowing up so fast without the exchange or clearing firm able to react or reverse positions, the firm itself could be in danger as well.”

For more details on current problems see the Financial Times article Credit Suisse fined over algo failures

NYSE Euronext revealed on Wednesday it had for the first time fined a trading firm for failing to control its trading algorithms in a case that highlights the pitfalls of the rapid-fire electronic trading that has come to dominate many markets.

The group, which operates the New York Stock Exchange, said it had fined Credit Suisse $150,000 after a case in 2007 when hundreds of thousands of “erroneous messages” bombarded the exchange’s trading system.

Asked if the exchange’s systems could have been knocked out, he said: “If you had multiplied this many times you’d have had a problem on your hands.”

Economist on fat tails and finance

A special report on the future of finance in The Economist Fallible mathematical models: In Plato’s cave:

… although the normal distribution closely matches the real world in the middle of the curve, where most of the gains or losses lie, it does not work well at the extreme edges, or “tails”. In markets extreme events are surprisingly common—their tails are “fat”. Benoît Mandelbrot, the mathematician who invented fractal theory, calculated that if the Dow Jones Industrial Average followed a normal distribution, it should have moved by more than 3.4% on 58 days between 1916 and 2003; in fact it did so 1,001 times. It should have moved by more than 4.5% on six days; it did so on 366. It should have moved by more than 7% only once in every 300,000 years; in the 20th century it did so 48 times.

In Mr Mandelbrot’s terms the market should have been “mildly” unstable. Instead it was “wildly” unstable. Financial markets are plagued not by “black swans”—seemingly inconceivable events that come up very occasionally—but by vicious snow-white swans that come along a lot more often than expected.

This puts VAR in a quandary. On the one hand, you cannot observe the tails of the VAR curve by studying extreme events, because extreme events are rare by definition. On the other you cannot deduce very much about the frequency of rare extreme events from the shape of the curve in the middle. Mathematically, the two are almost decoupled.

The drawback of failing to measure the tail beyond 99% is that it could leave out some reasonably common but devastating losses. VAR, in other words, is good at predicting small day-to-day losses in the heart of the distribution, but hopeless at predicting severe losses that are much rarer—arguably those that should worry you most.

When David Viniar, chief financial officer of Goldman Sachs, told the Financial Times in 2007 that the bank had seen “25-standard-deviation moves several days in a row”, he was saying that the markets were at the extreme tail of their distribution. The centre of their models did not begin to predict that the tails would move so violently. He meant to show how unstable the markets were. But he also showed how wrong the models were.

Modern finance may well be making the tails fatter, says Daron Acemoglu, an economist at MIT. When you trade away all sorts of specific risk, in foreign exchange, interest rates and so forth, you make your portfolio seem safer. But you are in fact swapping everyday risk for the exceptional risk that the worst will happen and your insurer will fail—as AIG did. Even as the predictable centre of the distribution appears less risky, the unobserved tail risk has grown. Your traders and managers will look as if they are earning good returns on lower risk when part of the true risk is hidden. They will want to be paid for their skill when in fact their risk-weighted returns may have fallen.