The Gaussian copula is not an economic model, but it has been similarly misused and is similarly demonised. In broad terms, the Gaussian copula is a formula to map the approximate correlation between two variables. In the financial world it was used to express the relationship between two assets in a simple form. This was foolish. Even the relationship between debt and equity changes with the market conditions. Often it has a negative correlation, but other times it can be positive.
That does not mean it was useless. The Gaussian copula provided a convienent way to describe a relationship that held under particular conditions. But it was fed data that reflected a period when housing prices were not correlated to the extent that they turned out to be when the housing bubble popped. You can have the most complicated and complete model in the world to explain asset correlation, but if you calibrate it assuming housing prices won’t fall on a national level, the model cannot hedge you against that happening.
→ The Economist
The best alternative, Turner thinks, is his radical proposal—creating money and handing it out to entities that can spend it. He readily concedes that it wouldn’t matter much whether the newly minted money was forwarded to households in the form of bank credits, or used to finance tax cuts, or spent on building new roads and bridges. The key point is that the government would be stimulating the economy without issuing any new debt. It wouldn’t be accentuating the problem of debt overhang, or creating the conditions for yet another boom-and-bust cycle.
→ The New Yorker
A short and informative recollection of what happened roughly three decades ago, from Barry Ritholtz :
Where were you on Monday, Oct. 19, 1987?
Today is the first time since 2009 that Oct. 19 has fallen on a Monday, and that has me thinking about that day. I recall exactly where I was — in graduate school, walking between classes, when I passed a television broadcasting the collapse.
New York Magazine had a great piece too on February 2008 :
It all started, of course, on Wall Street. On Black Monday, October 19, 1987, the Dow Jones index, for reasons still being debated, fell 508 points, almost a quarter of its total. (The current equivalent, for comparison’s sake, would be a 3,200-point loss on one day.) The drop turned out to be a “black swan event,” a weirdly poetic economist’s term meaning, basically, a fluke (though few people remember it, the Dow still eked out a positive finish for the year). Still, the hiccup seemed to foretell the instability to come. Over the next two years, with the economy perceived to be overheating, the Fed repeatedly jacked up interest rates, which made bonds and T-bills sexier than stocks, which triggered an epidemic of unscrupulous bond peddling, which further destabilized the market—leading to a slowdown. (If that all sounds disturbingly like the recent subprime-debt mess, well, that’s because it is. But more on that later.) And a slowdown on Wall Street, which provides over 20 percent of the city’s cash income, spells a slowdown for New York.
→ Bloomberg View
On consumer lending :
The most creditworthy customers, it turns out, are the least keen to splurge when extra credit is offered. For every dollar their credit limits increase, they boost their borrowing by $0.23. Even that is an exaggeration: by further digging through the data, the researchers establish that the borrowers with the best credit records are only shifting their borrowing from card to card to take advantage of improved terms—not borrowing any more in aggregate. At the other end of the scale, those with the muckiest credit histories borrow an extra $0.58 for every $1 hike in their credit limit.
But that is not the whole story. The researchers then take a bank’s perspective, and ask to whom it makes most sense to lend. Boosting credit limits draws in extra interest payments and charges, but there are costs too. If it is mainly the highest-risk borrowers who take advantage of higher limits, or if the higher limits encourage more reckless borrowing in general, then default rates will climb, eating away at profit margins.
→ The Economist
Follow-up on yesterday’s article about personality tests approval to get loans. Payoff adds a touch of algorithms on top of psychology to determine the creditworthness of the borrower.
Galen helped develop the matching algorithms at eHarmony, which are responsible for 4 percent of U.S. marriages. We’re using his expertise to bring psychology to finance. Our science is ultimately about behavior change and helping people make better financial decisions. This process of change begins with self-understanding, and we’re using advanced psychometric assessments to understand people’s mindsets— the goal being to improve how they approach their financial decisions. We’ve taken the hundreds of questions you might answer in a typical psychometric assessment and compressed them into a three-minute “gamified” online assessment that gauges your financial personality.
It’s much more likely that, if people want a loan, they will try and game the system. There is a strong chance they would give the answers that they think reflect a better credit trustworthiness: “I definitely pay attention to financial details. I am perhaps, if anything, too cautious.” As opposed to: “Oh, I don’t care, just give me the cash.” Any psychological assessment scheme would have to be robust to such game-playing, perhaps by asking more opaque questions.
→ The Conversation
Some of the deals Siegel made were hugely profitable, while others proved more troublesome. As he soon discovered, after creditors sell off unpaid debts, those debts enter a financial netherworld where strange things can happen. A gamut of players — including debt buyers, collectors, brokers, street hustlers and criminals — all work together, and against one another, to recoup every penny on every dollar. In this often-lawless marketplace, large portfolios of debt — usually in the form of spreadsheets holding debtors’ names, contact information and balances — are bought, sold and sometimes simply stolen.
→ New York Times Magazine