ducati998

From the previous post, comes a possible answer…

How frequent are Bear market rallies, before one takes hold? There were three in the last sustained downleg of the 2000 Bear market. What number are we onto currently?

Aluminium is in the news, certainly with the speculation surrounding GM and the governments position towards them 60 days hence.

Desjardins Securities, meanwhile forecast that base metal equities will move higher this year.

In another development, Charlie Aitken, director of Southern Cross Equities, was quoted in the Wall Street Journal as suggesting Alcoa would be a good takeover target for global miner BHP because Alcoa’s assets were cheap.

“Alcoa fits in all the BHP boxes in my view about tier one assets as well as leading global market shares,” said Aitken. “I find it highly interesting that there has been a huge volume spike in Alcoa shares and BHP has raised fresh debt capital that is almost identical in scale to Alcoa’s debt position.”

But Charles Bradford, an analyst in New York with Bradford Research, said a BHP move for Alcoa would not make sense, since there would be antitrust issues dating back nearly a decade to when Alcoa sold assets to BHP when it acquired Reynolds Metal.

Bradford also noted that metals stocks were down on Monday because of general unease at the situation at General Motors Corp , a big buyer of steel and aluminum.

“People may be feeling better today,” he said. On Monday, the Obama administration gave GM 60 days to reach deeper concessions with bondholders and the United Auto Workers union and said it would finance a court-supervised bankruptcy if the process failed to deliver deep enough savings.

CENX is as a high cost producer, highly leveraged to the price of aluminium, and, represents a good speculative play in this sector.

More evidence, if any was required, that mathematicians, should stay the hell away from Wall St…they are seemingly well out of their depth. Nothing more than simple addition, subtraction with the odd multiplication and division is required. As soon as advanced mathematics and calculus is invoked…watch out!

From Portfolio.com

A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li’s work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.

For five years, Li’s formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.

Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li’s formula hadn’t expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system’s foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.

David X. Li, it’s safe to say, won’t be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li’s Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.

How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.

A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there’s always some risk—the higher the interest rate the bond must carry.

Bond investors are very comfortable with the concept of probability. If there’s a 1 percent chance of default but they get an extra two percentage points in interest, they’re ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.

Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There’s no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There’s certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there’s no easy way to assign a single probability to the chance of default.

Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.

The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don’t affect the mortgage pool much as a whole: Everybody else is still making their payments on time.

But not all calamities are individual, and tranching still hadn’t solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there’s a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there’s a higher probability they will default, too. That’s called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.

Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.

Yet during the ’90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you’re talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.

To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let’s call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.

But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney’s parents get divorced, what are the chances that Alice’s parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.

If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.

But it’s a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.

In the world of mortgages, it’s harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation’s macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?

Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master’s degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master’s in actuarial science and a PhD in statistics, both from Ontario’s University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.

Li’s trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street’s ever more complex investment structures.

In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled “On Default Correlation: A Copula Function Approach.” (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.

If you’re an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn’t constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li’s paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.

When the price of a credit default swap goes up, that indicates that default risk has risen. Li’s breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It’s hard to build a historical model to predict Alice’s or Britney’s behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice’s and Britney’s default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).

It was a brilliant simplification of an intractable problem. And Li didn’t just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.

The effect on the securitization market was electric. Armed with Li’s formula, Wall Street’s quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li’s copula approach meant that ratings agencies like Moody’s—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.

As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn’t matter. All you needed was Li’s copula function.

The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.

At the heart of it all was Li’s formula. When you talk to market participants, they use words like beautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.

“The corporate CDO world relied almost exclusively on this copula-based correlation model,” says Darrell Duffie, a Stanford University finance professor who served on Moody’s Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world’s financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. “Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus,” wrote derivatives guru Janet Tavakoli in 2006.

The damage was foreseeable, and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that “the correlations between financial quantities are notoriously unstable.” Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn’t alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn’t perfect. Li’s approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford’s Duffie and ask him to come in and talk to them about exactly what Li’s copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.

In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn’t understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.

In finance, you can never reduce risk outright; you can only try to set up a market in which people who don’t want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn’t have any risk at all, when in fact they just didn’t have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.

Li’s copula function was used to price hundreds of billions of dollars’ worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.

Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.

“Everyone was pinning their hopes on house prices continuing to rise,” says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. “When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn’t rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO.”

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn’t know, or didn’t ask. One reason was that the outputs came from “black box” computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula’s weaknesses, weren’t the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

“The relationship between two assets can never be captured by a single scalar quantity,” Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It’s impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.

No one knew all of this better than David X. Li: “Very few people understand the essence of the model,” he told The Wall Street Journal way back in fall 2005.

“Li can’t be blamed,” says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.

Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. “People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked,” he says. “Co-association between securities is not measurable using correlation,” because past history can never prepare you for that one day when everything goes south. “Anything that relies on correlation is charlatanism.”

Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn’t talk without permission from the PR department. In response to a subsequent request, CICC’s press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.

In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years’ worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.

As Li himself said of his own model: “The most dangerous part is when people believe everything coming out of it.”

Very early days, and quite possibly meaningless, but certainly worth watching…Dr Copper is starting to potentially signal an upturn in economic activity.

We can see that the economic downturn built huge surpluses, that have just started to be drawn down…which has also correlated to the uptick in the stockmarket.

Just a look on a shorter term chart the drawdown of copper, economically very sensitive.

In all the major bottoms of the various Bear markets, 1929-1938, 1969-1975, 1979-1982, commodities stabilised first, with a time lag for the new Bull market to build.

This Bear market, that started in 1998, quietly, and blew up in 2000, bottomed in 2003, and then drove a massive Bear market rally through 2007, collapsing into our current situation and the concurrent Bull market in debt, is set to reverse.

A massive deleveraging, by all and sundry, implies that debt will require higher yields to make it attractive. Equity, or ownership, however, with attendant risks, in a deleveraging environment, becomes more attractive.

Stocks, generally speaking, are not yet trending higher. They are not yet making higher highs, and lower lows on the longer term charts of advances & declines.

Energy & Oil, however seem to have a fairly solid bottom in place. Energy is simply one of those commodities that will maintain demand pretty much through anything. Now is a pretty good time to be looking at a variety of energy plays. Personally I like oil exposure via the large multinationals.

Metals, a much more speculative play in the commodities arena. Steel again, a volatile sector along with aluminium. Certainly with the news out of Detroit yesterday, not a good look. However, if deficit spending materialises, then potentially we might see a good run.

Inventory-to-Sales, spiked with the consumer closing his wallet. Increasing unemployment will not encourage those still with jobs to spend. Until we see I/S ratios falling, we will have weak earnings across these sectors. This of course does not preclude a stock market rally in anticipation of improving earnings, and trailing aggregate P/E ratios indicating value, once a recovery is underway.

From The Economist:

COMPARISONS to the Depression feature in almost every discussion of the global economic crisis. In world trade, such parallels are especially chilling. Trade declined alarmingly in the early 1930s as global demand imploded, prices collapsed and governments embarked on a destructive, protectionist spiral of higher tariffs and retaliation.

Trade is contracting again, at a rate unmatched in the post-war period. This week the World Trade Organisation (WTO) predicted that the volume of global merchandise trade would shrink by 9% this year. This will be the first fall in trade flows since 1982. Between 1990 and 2006 trade volumes grew by more than 6% a year, easily outstripping the growth rate of world output, which was about 3% (see chart 1). Now the global economic machine has gone into reverse: output is declining and trade is tumbling at a faster pace. The turmoil has shaken commerce in goods of all sorts, bought and sold by rich and poor countries alike.

It is too soon to talk of a new protectionist spiral. Nevertheless, errors of policy risk making a bad thing worse—despite politicians’ promises to keep markets open. When they met in November, the leaders of the G20 rich and emerging economies declared that they would eschew protectionism and will doubtless do so again when they meet on April 2nd. But this pledge has not been honoured. According to the World Bank, 17 members of the group have taken a total of 47 trade-restricting steps since November.

Modern protectionism is more subtle and varied than the 1930s version. In the Depression tariffs were the weapon of choice. America’s Smoot-Hawley act, passed in 1930, increased nearly 900 American import duties—which were already high by today’s standards—and provoked widespread retaliation from America’s trading partners. A few tariffs have been raised this time, but tighter licensing requirements, import bans and anti-dumping (imposing extra duties on goods supposedly dumped at below cost by exporters) have also been used. Rich countries have included discriminatory procurement provisions in their fiscal-stimulus bills and offered subsidies to ailing national industries. These days, protectionism comes in 57 varieties.

There are good reasons for thinking that the world has less to fear from protectionism than in the past. International agreements to limit tariffs, built over the post-war decades, are a safeguard against all-out tariff wars. The growth of global supply chains, which have bound national economies together tightly, have made it more difficult for governments to increase tariffs without harming producers in their own countries.

But these defences may not be strong enough. Multilateral agreements provide little insurance against domestic subsidies, fiercer use of anti-dumping or the other forms of creeping protection. Most countries are able to raise tariffs, because their applied rates are below the maximum allowed by their WTO commitments. They may choose to do so despite the possible disruption to global supply chains. And because global sourcing amplifies the effect of tariff rises, even action that is permissible under WTO rules could cause a lot of damage. The subtler variants of protection may be similarly disruptive.

The gears of globalisation
The immediate cause of shrinking trade is plain: global recession means a collapse in demand. The credit crunch adds an additional squeeze, thanks to an estimated shortfall of $100 billion in trade finance, which lubricates 90% of world trade.

Just as striking as the speed of the downturn in trade is its indiscriminate nature. The World Bank has January trade data for 45 countries (available figures for G20 countries are shown in chart 2). These are values, expressed in American dollars, and so have been depressed not only by lower volumes but also by falling prices and a stronger dollar. The exports of 37 of these 45 countries were more than a quarter lower than in January 2008. Countries as diverse as Ecuador, France, Indonesia, the Philippines and South Africa saw exports drop by 30% or more. Commodity exporters, such as Argentina, have suffered with sellers of sophisticated manufactures, such as Germany and Japan.

Kei-Mu Yi, an economist at the Federal Reserve Bank of Philadelphia, argues that trade has fallen so fast and so uniformly around the world largely because of the rise of “vertical specialisation”, or global supply chains. This contributed to trade’s rapid expansion in recent decades. Now it is adding to the rate of shrinkage. When David Ricardo argued in the early 19th century that comparative advantage was the basis of trade, he conceived of countries specialising in products, like wine or cloth. But Mr Yi points out that countries now specialise not so much in final products as in steps in the process of production.

Trade grows much faster in a world with global sourcing than in a world of trade in finished goods because components and part-finished items have to cross borders several times. The trade figures are also boosted by the practice of measuring the gross value of imports and exports rather than their net value. For example, a tractor made in America would once have been made from American steel and parts; it would have touched the trade data only if it was exported. Now, it may contain steel from India, and be stamped and pressed in Mexico, before being sold abroad. As a result, changes in demand in one country now affect not just the domestic economy but also the trade flows and economies of several countries.

This mechanism can be seen at work in recent data—for instance, says Mr Yi, in American automotive-trade figures for the last three months of 2008. Imports from everywhere fell by about 20%. On the export side, sales to America’s partners in the North American Free Trade Agreement (NAFTA) fell by 20% whereas those to non-NAFTA countries rose slightly. This, he argues, is because three-quarters of exports to non-NAFTA countries consist of finished vehicles, whereas 60% of exports to NAFTA partners consist of parts and components, most of which return to the United States embodied in imported vehicles. So American exports to other NAFTA countries are to a large extent determined by America’s own demand for cars.

By making trade flows more sensitive to falls in output, vertical specialisation may provide some insurance against widespread protectionism. Manufacturers that rely on imported inputs may resist higher tariffs because they push up the prices of those inputs, making domestic industry less competitive.

Governments using tariffs as trade weapons now have to calculate the consequences far more carefully. This is borne out, for example, by Mexico’s response this month to the suspension by America of a NAFTA programme that allowed some Mexican truckers to carry goods north of the border. Mexico raised some tariffs, but by less than NAFTA rules allowed, and chose the goods carefully in order to limit the damage to its own industries.

Nevertheless, there is plenty of evidence that developing countries, at least, continue to use tariffs extensively. In the World Bank’s study, tariff increases accounted for half of the protective measures by these countries. Ecuador raised duties on 600 goods. Russia increased them on used cars. India put them up on some kinds of steel. Developing countries have more scope for raising tariffs without breaking WTO rules than richer ones do, because the gap between their applied rates and the ceilings they agreed to is greater than for developed countries.

When governments do impose tariffs, vertical supply chains amplify their effects. Because tariffs are typically levied on the gross value crossing the border (with some exceptions, such as exports from Mexican maquiladoras), trade responds more to changes in tariffs—down or up—with global supply chains than without.

But there is another, more subtle reason to worry about even small rises in tariffs. Theoretical models that incorporate vertical specialisation find that it takes off only when tariffs fall below a threshold level. Once this happens, however, trade explodes, so that a slight lowering of trade barriers can cause a huge increase in trade. By the same token, if tariffs rose above a certain point—which might be below the maximum agreed on at the WTO—global supply chains would disintegrate. Trade would drop even more steeply than it has in recent months.

That said, supply chains need not snap so easily. Even if tariffs go up, other costs that determine the viability of supply chains may go down: the price of oil (and hence the cost of transport) has fallen a long way in the past year. Firms have invested a lot in their supply chains and will be loth to abandon them. And if global supply chains do survive, vertical specialisation could help trade recover speedily when demand returns.

Although increased tariffs are a cause for concern, they are far from the only form of protection being used in this crisis. Two-thirds of the trade-restricting measures documented by the World Bank are non-tariff barriers of various kinds. As with tariffs, developing countries are the principal wielders of these weapons.

Indonesia has specified that certain categories of goods, such as clothes, shoes and toys, may be imported through only five ports. Argentina has imposed discretionary licensing requirements on car parts, textiles, televisions, toys, shoes and leather goods; licences for all these used to be granted automatically. Some countries have imposed outright import bans, often justified by a tightening of safety rules or by environmental concerns. For example, China has stopped imports of a wide range of European food and drink, including Irish pork, Italian brandy and Spanish dairy products. The Indian government has banned Chinese toys.

In addition, anti-dumping is on the increase. The number of anti-dumping cases initiated at the WTO had been declining, but it started to pick up in the second half of 2007. The data for 2008 are not yet complete but Chad Bown, an economist at Brandeis University, estimates that the number was 31% higher than in the previous year. The number of cases ending with extra duties went up by 20%. India was the biggest initiator of anti-dumping action, and America and the European Union imposed duties most frequently.

Rich countries’ weapon of choice so far is neither tariffs nor non-tariff barriers to imports. They have been keen users instead of subsidies to troubled domestic industries, particularly carmakers. Some economists, such as Gene Grossman, of Princeton University, cite this as evidence that global sourcing has changed the political economy of protection. The American automotive industry no longer lobbies for direct protection, as it used to, because it imports much of its value-added and competes with foreign firms that assemble their cars in America. Carmakers now prefer explicit subsidies, and the world is replete with examples. Besides America, Argentina, Australia, Brazil, Britain, Canada, China, France, Germany, Italy and Sweden have all also provided direct or indirect subsidies to carmakers. The World Bank reckons that proposed subsidies for the car industry amount to $48 billion. Nearly 90% of this is in rich countries, where it can easily be slipped into budgetary packages to stimulate demand.

The worry about such subsidies is that they could cause production to switch from more efficient plants (eg, in central and eastern Europe) to less efficient ones in rich countries with deep pockets (eg, in western Europe). Whether the location of output is shifting is not yet clear, but politicians plainly hope it will. On March 19th Luc Chatel, the French industry minister, boasted that Renault’s plans to create 400 jobs at a factory near Paris by “repatriating” some production from Slovenia was the result of government aid. Renault has denied this, saying that it was at full capacity in Slovenia.

There are some international rules to prevent distorting subsidies. The EU has regulations to limit state aid, and is looking into its members’ assistance to carmakers. Gary Hufbauer, of the Peterson Institute for International Economics in Washington, DC, argues that American subsidies transgress WTO norms.

Helpful ambiguity
However, WTO action against subsidies is not straightforward. To complain successfully, a country has to show that a subsidy meets several criteria. Then there is a pots-and-kettles problem: having subsidies of your own does not stop you from challenging someone else’s, but if you pick a fight they may have a go at yours. This uncertainty and ambiguity only adds to subsidies’ attraction. Governments can aid their carmakers and at the same time criticise others for their protectionist ways.

Protectionist urges are also being bolstered by countries’ seeming inability to co-ordinate their fiscal stimulus programmes. Some countries have been reluctant to work the budgetary pump for fear that their extra demand will leak abroad to the benefit of foreigners. To stop the seepage, some governments have inserted discriminatory conditions into their fiscal programmes, the prime example being the “Buy American” procurement rules. These were weakened after protests and threats of retaliation from abroad, but not before the prospects for global co-operation had been dented. Greater co-ordination of fiscal expansion would ease governments’ worries about leakage, because everyone else would be leaking too: all would gain from each other’s spending.

What should world leaders do to stop protection fraying the threads that tie the world economy together? The pious declaration at the previous G20 meeting has had little effect. There is a risk that another such promise on April 2nd will prove to be just as empty. The difficulty lies in devising something comprehensive and detailed enough to address the variety of protectionist measures that are being deployed in the crisis, and doing it quickly enough to maintain open trade.

Many argue that the most important thing for world leaders to do is to pledge a quick completion of the Doha round of trade talks, which stalled for the umpteenth time last summer. By reducing tariff ceilings, this would place tighter limits on countries’ ability to increase tariffs. It would also ban export subsidies in agriculture, which are being used with greater vigour, especially as prices of farm goods fall. The EU, for example, has announced new export subsidies for butter, cheese and milk powder. Most important, completing Doha would be the clearest and most tangible evidence possible of a commitment to consolidating and building on the gains from more open trade secured in successive rounds since the second world war.

Some economists disagree. Aaditya Mattoo, of the World Bank, and Arvind Subramanian, of the Peterson Institute, argue that the Doha round is too ambitious given the state of the world economy, because it seeks to open markets for rich countries’ manufactured goods just when the politics are against it. At the same time, they point out that Doha would not restrict the use of some non-tariff measures causing most concern, such as the Buy American provisions or subsidies for failing industries. Messrs Mattoo and Subramanian suggest a new “crisis round” of world trade talks. In the first instance, WTO members could commit themselves to a standstill on all forms of protectionism.

Several other economists have also proposed a standstill. However, Messrs Mattoo and Subramanian suggest that in order to give governments a political reason to agree to this, they should also be allowed to postpone further liberalisation for the duration of the crisis. They would then embark on a new round instead of Doha, which would address the forms of protection that now look most pressing.

But the appetite for starting yet another series of talks is likely to be limited. Even if the crisis round’s agenda were more realistic than Doha’s (which isn’t obvious), there would be no guarantee that it could be concluded quickly enough to stop the bleeding in global trade.

Whatever they think about Doha or about the idea of a crisis round, most economists will agree that a simple promise to resist protectionism will not suffice. Some thing more specific is needed. A good start would be for governments, beginning with the leaders of the G20, to draw up a comprehensive list of protectionist measures that goes beyond tariffs and export subsidies. They could then agree to go no further with these than they have already.

Next, an agreement on co-ordinating fiscal policy would go a long way towards making such a standstill commitment credible, because it would alleviate worries about leakages abroad. Finally, empowering the WTO to name those who break the standstill would help to underpin it. The threat of embarrassment may make some countries think twice.

During the Depression, the volume of world trade shrank by a quarter. Nothing like that has been seen or forecast so far. Yet one lesson from the worldwide economic distress of three-quarters of a century ago is that once trade barriers come up, they take years of negotiation to dismantle. Preventing protectionism from getting worse is preferable to having to repair the damage afterwards. And even if a full-blown trade war can be ruled out, death by a thousand cuts cannot. The costs of myriad piecemeal measures could still add up to damaging protectionism. And when demand does eventually revive, if the world economy is supported by an open system of trade, it will recover all the faster.

From The Economist:

WHEN the going gets tough, the solvent get buying. That, roughly, was the philosophy of the titans of the oil industry last time the price of their product plummeted, in the late 1990s. Hunting for oil had become less profitable thanks to the falling price, whereas preying on rivals had become easier, thanks to plunging share prices. Thus Exxon Mobil, Chevron Texaco, BP Amoco and TotalFinaElf were born. So when Suncor and Petro-Canada, two big Canadian oil firms, announced a C$19.3 billion ($15.8 billion) merger on March 23rd, the industry’s biggest since 2006, speculation mounted that another wave of deals might be imminent.

Suncor and Petro-Canada say the merger will help them withstand the trying times. The bosses of the two firms think they can save C$300m a year on operating costs and C$1 billion a year in capital expenditure. These savings are possible thanks chiefly to the two firms’ complementary investments in Canada’s tar sands, where oil is found in a particularly impure and viscous form, requiring a lot of expensive processing. The two firms plan to share pipelines and other infrastructure at existing facilities and future ones.

The two companies also hope their merger will help reduce inflation in the oil-sands region, which took off amid a mad rush to develop new projects when the oil price was high. Petro-Canada recently delayed a development called Fort Hills after the projected cost rose from C$14 billion to C$25 billion in just over a year. It believes the merger will help reduce the bill again by dampening competition for scarce labour and equipment.

Tar-sands operations are a natural focus for consolidation. They are expensive to run and so are more exposed to the falling oil price. At the very least, tar-sands firms will struggle to finance expansions with the oil price so low. But Canada’s tar sands remain an attractive investment because they provide long-lived reserves in a stable country—a rarity in the oil business these days. That is why Total (as it is now known) recently offered to buy UTS Energy, a tiddler that is one of Petro-Canada’s partners in the Fort Hills project.

Much the same logic applies to takeovers of coal-bed methane firms, which extract natural gas from coal deposits. BG, a British gas firm, has just succeeded in buying an Australian coal-bed methane firm, Pure Energy Resources, for A$1 billion ($729m). Last year it spent A$6 billion on another, Queensland Gas. ConocoPhillips and Royal Dutch Shell have also made recent investments in coal-bed methane.

Indeed, over 40% of takeovers in the oil and gas business last year involved “unconventional” resources such as tar sands and coal-bed methane, according to Chris Sheehan of IHS Herold, a research firm. He expects more such deals this year, as the industry’s giants snap up unconventional reserves that their smaller, cash-strapped owners cannot afford to develop.

But Mr Sheehan does not expect the giants of the industry to snap one another up, as they did in the blockbusting deals of the 1990s. For one thing, they are already so big that further mergers would raise competition concerns. Moreover, with the exception of Exxon Mobil, which has tens of billions stashed away, they would struggle to raise the money. Instead, middling firms are likely to be the main targets. Rumours are rife, for example, of an imminent bid for Santos, an Australian firm with several liquefied natural gas projects in the works. And the Western oil majors are not the only buyers on the prowl: in recent years state-owned oil firms from countries such as China and India have become much more acquisitive.

From Yahoo Finance:

HOUSTON (AP) — Plunging crude prices have begun to play out in favor of Western oil companies in one regard, giving them leverage with oil-rich countries that only months ago had no reason to compromise.

Countries like Venezuela, Libya and Russia have kept a tight grip on their vast oil reserves in recent years as crude prices soared above $100 per barrel, translating into big revenues. Much of that money, rather than going back into the oil industry, was spent on unrelated political and social programs.

At $50 per barrel, these countries are far more constrained and can’t adequately fund some oil and gas projects.

Experts say Western oil corporations, who stockpiled cash when profits were flush, can shift operations to any corner of the globe and have the capital that allows them to strike deals with state-controlled producers on very favorable terms. When that might happen is uncertain, but energy experts and some executives have said deals are imminent if prices remain at current levels.

The new opportunities for the major Western oil companies come even as the United States attempts to shift away from fossil fuels. Just this week, President Barack Obama said the nation must free itself “from the dangerous dependence on foreign oil.”

Still, the International Energy Agency and other forecasters have said fossil fuels will be the primary source of global energy for decades to come, and national oil companies control roughly three-quarters of the world’s proven reserves. So, for Western oil companies, it makes sense to strike deals in places where they know there’s crude.

When oil was $100 a barrel, “these national companies were saying, ‘What the hell do we need the majors for? We’ve got gobs of cash,’” said Amy Jaffe, an energy expert at Rice University’s James A. Baker III Institute for Public Policy.

Now, some fields are not economical to develop with oil at current prices. In other cases, crumbling infrastructure makes the work more difficult and expensive, if not impossible, for state-run companies.

“As the economy and prices collapsed, it’s a different picture,” Jaffe said.

Chevron Corp., Royal Dutch Shell PLC and others have said they expect greater access to reserves in countries with nationalized oil and gas industries, something that could help them stem declining production.

But there are risks involved.

As recently as two years ago things went so badly in oil-rich Venezuela that Exxon Mobil and ConocoPhillips fled the country, leaving behind billions in assets. Libya’s Moammar Gadhafi threatened earlier this year to nationalize his oil industry — again.

The reaction to oil prices, which recently hit five-year lows, is varied by country.

Saudi Arabia, the OPEC powerhouse, said it won’t cut funding at its state-run Aramco. But oil production is dropping in other countries, slashing revenues that fund national budgets. Contractors are shutting down rigs in Venezuela because the government has stopped paying them.

“The longer we’re in this global downturn with weaker prices, the more those governments are going to need the (international oil companies),” said Dan Yergin, an author and chairman of Cambridge Energy Research Associates, an energy consultancy.

The world’s big oil corporations already do some business in countries with nationalized industries, but the terms are often slanted heavily in the country’s favor. In the current environment, Yergin said, companies like Exxon and Shell will be looking for more favorable and “durable contracts, where the rules won’t change.”

In particular, they’ll be looking for deals that offer lower royalty and tax rates and a greater share of production.

“We’re always looking for other large resource owners and national oil companies that might be situated where we could bring a lot of value,” Exxon Mobil chairman and chief executive Rex Tillerson told Wall Street analysts this month.

Even with the Obama administration’s stance on oil dependency — and the potential backlash for doing business with leaders like Venezuela’s Hugo Chavez — closer partnerships with oil-rich countries could help avert energy spikes like the one last summer, when gasoline in the U.S. soared above $4 a gallon for the first time.

The IEA has said more than a trillion dollars in annual investments will be needed for the next two decades to expand the supply of oil and avoid an energy crisis that could choke the global economy.

The Paris-based energy watchdog said state-run companies are projected to account for about 80 percent of the increase of both oil and natural gas production to 2030.

The potential for Western oil producers to sign new exploration and production deals with resource-rich nations comes as oil majors are finding it increasingly difficult to find new reserves — in part from limited access — and boost production. Big Oil output, in fact, has largely been in decline in the past few years.

And it doesn’t look like the Obama administration is going to open up any new acreage at home anytime soon.

“That brings into question: Can you produce more here?” said John Felmy, chief economist for the American Petroleum Institute, the oil industry’s trade association. “Certainly, (U.S. producers) would love to do that first and foremost, but they’re not going to sit on their hands.”

Still, no one is likely to rush into new agreements without some assurance of stability.

The American chief executive of a joint venture between a Russian oil company and BP PLC was forced to flee Russia last year because officials there would not renew his visa.

Additionally, Exxon Mobil and ConocoPhillips remain in international arbitration nearly two years after Venezuela’s Chavez nationalized that country’s last privately run oil fields in Orinoco, shouting “Down with the U.S. empire!” as Russian-made fighter jets streaked overhead.

ConocoPhillips wrote off $4.5 billion in assets lost to Venezuela.

Other companies including Chevron remained. The government reduced them to minority partners.

Now, Venezuela is again pursuing foreign investment to help it pump oil. So is Iraq as it tries to reinvent its oil industry, sweetening contracts terms but demanding that oil companies start drilling now.

Analysts say Brazil is among those also likely in need of foreign investment to spur oil and gas development.

“The lessons are very, very recent,” Yergin said. “Let’s put it this way: The (international oil companies) are going to pay an awful lot of attention to their arbitration clauses this time around.”