The Credit and Sovereign Debt Crises Prof. Anne Sibert MSc Financial Economics May 2012
Who is to blame for the financial crisis?How is it related to the sovereign debt crisis?
US presidents and the US Congress: for following a policy of encouraging home ownership • Fannie Mae was founded in 1938 and chartered by Congress as a government-sponsored enterprise (GSE) in 1968. • Fannie Mae puchases and securitises loans from approved mortgage sellers. For a fee, Fannie Mae guarantees the timely payment of interest and principal. • Freddie Mac is a GSE founded in 1970. It buys mortgages on the secondary market and securitises them. • In 2008, Fannie Mae and Freddie Mac owned or guaranteed about half of the $12 trillion worth of mortgages outstanding in the United States. • Not officially backed by the government, but believed to be implicitly backed; exempt from state and local taxes. (An off-shoot of Fannie Mae named Ginnie Mae explicitly guaranteed some mortgages to veterans and state employees.)
What happened next In Sept 2008, Fannie Mae and Freddie Mac were placed in receivership. The government provided a $187.5 billion rescue package. Thanks to rising house prices they have since paid $185.2 billion in dividends to the government.
Increasing risky lending, loans and lending practices were tolerated. • In 1994, one in twenty new mortgages was subprime; by 2006 it was one in five. • Risky loans: no down payment; adjustable interest rate; interest only for some period. • Loans were made without verifying assets and income of borrowers.
Risky loans to risky borrowers were encouraged • The Community Reinvestment Act of 1977 (Carter administration): forced banks to open new branches in areas of extreme poverty in inner cities and to make home mortgages loans in these areas. • In 1999 (Clinton administration) Lenders under pressure to increase loans to poor and moderate income borrowers in inner cities. Lenders pressured Fannie Mae to ease its credit requirements on the loans it was willing to purchase. • In 2002 (Bush 2 administration) "Renewing the Dream”: tax credits for building affordable housing in distressed areas. Increased funding for other programmes aimed at increasing home ownership for low-income Americans.
Events abroad were to blame Current Account Surpluses in the Developing World (in $ billion)
Why? • Distortions in China led to too high saving. • Rising oil prices led to a windfall gain in Middle Eastern countries. They did not have enough absorptive capacity to invest it at home.
Factors causing too high saving in developing countries resulted in too low saving in the United States • Equity prices rose in the United States as capital flowed in. This cause perceived US wealth to rise and consumption went up; saving went down. • After the dot com bubble burst in 2000, real interest rates declined in the United States. As the US is a debtor country, both the income and substitution effect went the same way. This probably caused saving to go down.
House prices went up • When the real interest rate when down the discounted stream of future rents went up and house prices went up. • Housing wealth is readily collateralisable in the United States and previously credit-constrained households spent more and saved less.
Saving Glut Hypothesis • This is called the savings glut hypothesis; it is associated with Bernanke. • Flows of foreign capital into the US were matched by lower US saving through, first, a rise in equity prices and, later, a fall in the real interest rate. • The fall in the real interest rate was associated with an increase in house prices.
Was the Fed to blame for the house price boom? • The Fed controls a short-term nominal interest rate. • Because of distortions, it can affect the real interest rate in the short run. • Monetary policy cannot systematically affect real variables such as the real interest rate. • The Fed did not cause the house price boom.
Was it a bubble? Should the Fed have popped it? • We don’t know if it was a bubble. • Bubbles are deviations from the price path implied by the fundamentals. Would a small change in a fundamental help? • Monetary policy is not the right tool for popping bubbles.
Why did capital flow into the US and not into Europe? Table 1. Ease of Doing Business Index, 2007
The US may have been perceived as a more attractive place to invest • During the period 1997 – 2006, relatively rigid labour and product markets, more poorly functioning credit markets, higher costs of starting and closing businesses and more restrictions on land use and business hours may have made the EU a less attractive place to invest than the United States. • The relatively business-friendly environment of the United States has been especially important over the last two decades. When technological innovations in semiconductor manufacturing led to the information and communications technology (ICT) revolution, the relatively unregulated product markets and flexible labour markets in the United States enabled a rapid restructuring of the economy to make the best use of ICT in other industries
The dollar is the world’s premier reserve currency • A significant fraction of capital inflows in the United States are believed to be official holdings. • Between 1999 and Aug 2007, Chinese foreign exchange reserves increased from about $160 hundred million to $1.4 trillion. • Other Asian countries also increased their reserve holdings following the Asian crisis. Most of these increases were probably in dollars. • It is estimated that about two-thirds of the world’s reserves are in dollars. • Along with high productivity in the United States, relative to the rest of the world, the dollar is a convenient currency for central banks to hold. • Size, liquidity and depth of financial markets: At the end of 2005, the outstanding stock of US government securities was $4.2 trillion, compared with $4.7 trillion for the euro area. But, unlike the euro area, US government securities were all perceived to be of high quality (rated AAA by Fitch). Moreover, the markets for US government securities are more liquid and have greater depth than those in the euro area.
Twin Deficits Hypothesis • Current Account Deficit = Private financing deficit + Government Budget Deficit • This theory seems particularly ill-suited to the period under discussion as the US government budget was in surplus between 1996 and 2000 and Germany and Japan have run government budget deficits and their current accounts have been in surplus.
Rising house prices fueled securitisation • Once upon a time, bankers (and other lenders) who made loans to a house purchaser, or other borrower, retained the default risk. • Hence, they had an incentive to collect information on the borrowers and to monitor their subsequent behaviour. • Banks did not like holding illiquid assets, however. • The innovation of securitisation gave bankers the opportunity to sell their mortgages, passing off the risk and obtaining new liquidity with which to make additional loans. • Banks no longer had much of an incentive to screen or oversee their borrowers; they made riskier types of loans to less credit-worthy borrowers.
Securitisation When the banks sold their mortgages, it was to off-balance-sheet special-purpose vehicles (SPVs). These SPVs combined the mortgages with other assets and issued tranched securities backed by the entire pool. These securities were then purchased by other SPVs that combined them with various assets in the next level of securitisation. By the time a conduit of a German Landesbank sold some tranche of a security backed by mortgages, sliced and diced and bundled and rebundled with credit card receivables, automobile loans and other assets, to a London hedge fund, neither borrower nor seller had much of a clue about the nature of the underlying assets. Nor did they appear to be overly concerned. As long as US house prices would continue to rise, all would be well.
When the boom ended • Highly leveraged home owners began to default on their mortgages, eroding the value of mortgage-backed securities. • High degrees of leverage in the financial sector magnified the effect of changes in asset prices on the balance sheets of financial institutions and the financial crisis began.
The search for yield • Paulson (2008) has advanced a complimentary reason why the fall in real interest rates helped fuel the crisis: low interest rates led to excessive risk taking and a global search for return. Presumably, this is because bankers and employees of other financial firms have an asymmetric loss function which causes them to search for investments that have the possibility of high upside returns. • A related reason for greater risk taking is that a long period of good monetary policy lowered volatility in financial markets, triggering a search for investments with risky returns.
Where were the supervisors and regulators? • In the United States, regulators were lax in permitting unbridled securitisation. Originally seen as a way of pooling risk, it caused financial institutions to make riskier loans. • Around the world, but especially in Europe, regulators were to blame for allowing financial institutions to become as large and as leveraged as they were. • Lawmakers in the United States were culpable for allowing a patchwork institution-based regulatory system to prevail.
“The odds of a meltdown are one in 10,000 years.”The Ukrainian Minister of Power, February 1986
Why do market participants make systematic errors? • Bankers rashly bet that the US house bubble would continue long after economists predicted its demise. • Managers of insurance companies and pension funds did not exercise due diligence when they purchased collateralised debt obligations and mortgage-backed securities that they did not understand. • Bankers were over confident and they took on too much risk.
“Hurrah, boys, we’ve got them!” General Armstrong Custer at the Little Big Horn
Overconfident Bankers • “We were hitting on all 99 cylinders, so you have to ask yourself, What can we do better? And I just can’t decide what that might be … Everyone says that when the markets turn around, we will suffer. But let me tell you we are going to surprise some people this time around. Bear Stearns is a great place to be.” James E. Cayne, Chairman and CEO of Bear Stearns, 2003.
The effects of overconfidence • Odean (1998), Barber and Odean (2001), Biais, Hilton, Mazurierand Pouget (2005) demonstrate that overconfidence can lead to excess trading and lower profits. • Daniel, Hirshleifer and Subrahmanyam (1998), Scheinkman and Xiong (2003) and Burnside, Han, Hershleifer and Wang (2011) show that it can lead to asset price anomalies such as overreactions, excess volatility and bubbles.
Some questions from Kahneman (2011) Kahneman, D., Thinking, Fast and Slow, London, Allen Lane, 2011. An individual has been described by a neighbour as follows: “Steve is very shy and withdrawn, invariably helpful but with little interest in people or the world of reality. A meek and tidy soul, he has a need for order and structure and a passion for detail.” Is Steve more likely to be a librarian or a farmer?
Fast and frugal heuristics • Humans have evolved to form impressions, make judgements and invent explanations quickly: Fast and frugal heuristics • A snap judgement: “This is dangerous” may be more apt to keep us alive than a slower and more finely nuanced thought. • But, the heuristics that promote speed may cause systematic biases.
An example of a heuristic • The representativeness heuristic is where we judge something by comparing it to our mental picture of a category. • An example from Myers (2008): “Linda, who is 31, single, outspoken, and very bright. She majored in philosophy in college. As a student she was deeply concerned with discrimination and other social issues, and she participated in anti-nuclear demonstrations. Based on that description, would you say it is more likely that • Linda is a bank teller • Linda is a bank teller and active in the feminist movement
Representativeness Heuristic • Most people say that the answer is b. (Mellor et al, 2001) But that cannot possibly be right! • Some other examples: (Myers, 2008) • Do more people live in Iraq or Tanzania? People usually answer according to how readily Iraqis and Tanzanians come to mind. • Vivid, easy to imagine events (shark attacks) seem more likely than hard-to-picture events. • People are quick to infer general conclusions from a single striking event: People switched from air travel to car travel after 11 September 2001.
Kahneman’s (2011) explanation • A person’s mental life can be described by the metaphor of two internal agents. The first produces that fast and frugal heuristics; the second produces slow and deliberative thought. Call these System 1 and System 2, respectively. • System 1 can be very good. A team of fire fighters is trying to douse a fire in a kitchen when the chief, without knowing why, heard himself yell to get out. Immediately after the floor collapsed. Only later did he realise that the fire had been unusually quiet and his ears unusually hot. The heart of the fire was in the basement below.
Another problem from Kahneman (2011) A bat and a ball cost $1.10. The bat costs one dollar more than the ball. How much does the ball cost?
System 1 vs. System 2 • Do the math: • Let bat := price of the bat ball := price of the ball bat + ball = 1.10 bat = ball + 1.00 This implies that 2 x ball + 1.00 = 1.10 or that 2 x ball = 10 cents or that ball = five cents. Thus the bat cost 1.05. • Probably everyone who got this right immediately thought of the answer that the bat cost a dollar and the ball ten cents, but they managed to resist it.
Another example from Kahneman (2011) ANN APPROACHED THE BANK
System 1 is not good at thinking statistically. • Going back to the introverted Steve. Our System 1 thinking associated Steve’s personality traits with those of a librarian. • Unless our System 2 overrules our System 1 we will say that Steve is more likely to be a librarian. • There are far more farmers than librarians. It is more likely that Steve is a farmer.
Another example from Kahneman (2011) A study of the incidence of kidney cancer in 3,141 counties of the United States reveals a remarkable pattern. The counties in which the incidence is lowest are mostly rural, sparsely populated and located in traditionally Republican states in the Midwest, the South and the West. What do you make of this?
But it is also true that: The counties in which the incidence of kidney cancer is highest are mostly rural, sparsely populated and located in traditionally Republican states in the Midwest, the South and the West. What do you make of this?
The key feature is “sparsely populated” Suppose an urn is filled with marbles. One fifth of the marbles are red and four-fifths are blue. Suppose we consider a scenario where we draw three marbles. The probability that all are red is 1/125. In a scenario where we draw seven marbles the probability that they are all red is 1/78,125.
Overconfidence • Also known as the “Lake Woebegone Effect”. • Overconfidence is pervasive. • Fischhoff (1977) found that when people claimed to be 100 percent confident, they were right 70 – 80 percent of the time. • Most of us are sure that we are better drivers than average. Svenson (1891), for example, found that 80 percent of survey respondents claimed to be in the top 30 percent of all drivers.
More Experiments Kahneman, D. a nd Tversky, A. (1797), “Intuitive Prediction: Biases and Corrective Procedures,” Managnement Science 12, 313-327. Daniel Kahneman and Amos Tversky (1979) asked people to fill in the gaps in statements such as “I fell 98 percent certain that the air distance between New Delhi and Beijing is more than ___ miles but less than ___ miles.” About 30 percent of the time the answers lay outside the range they felt 98 percent confident about.
Confirmation Bias • Overconfidence may lead to a failure to look for disconfirming evidence. • In psychology experiments, the failure of subjects to look for disconfirming evidence has “raised more doubts over human rationality than any other psychological tasks.” Oaksford and Chator (1993)
Wason’s test (P.C. Wason, 1960) • Wason gave participants in his test a sequence: 2,4,6. He asked participants to guess the rule that generated the sequence. • The rule was: Any three ascending numbers. • The subjects were allowed to discover the rule by generating sequences of three numbers. They were then told whether there sequence conformed to the rule or not.
23 of 29 particpants got it wrong • Participants typically formed a belief about what the rule was: say, counting by twos. • They then tested this by seeking for confirming evidence, rather than attempting to look for evidence that might disprove their theories.