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
Buffett has taken the criticism from these fellow giants of finance in his stride, responding with trademark wit and humour. He even compared himself to an orang-utan flipping coins. Joking aside, this is a testable hypothesis: Is Buffett’s performance better than chance? To test it, we will stand on the shoulders of another giant: Jacob Bernoulli.
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Again it is a very small number, but we can use our formula to calculate its value:
The expected value is much smaller than 1, so we can conclude that Buffett is a better investor than the luckiest orang-utan. If stock returns really do follow a random process – as Eugene Fama asserted – then Warren Buffett is more than just lucky. Compared with his competitors in the S&P 500, he’s brilliant.
Many different themes run through Deaton’s work – one of which is an emphasis on the importance of measurement. In his view, data collection and economic theory have become too separated, to the advantage of neither the data collector nor the economic theorist. Collectors need the guidance of theory and analysts need to understand the data they work with. Too often, Deaton says, “what we think we know about the world is dependent on data that may not mean what we think they mean”.
The genesis of the Pie Chart :
Some scholars believe the pie chart may have been inspired by the use of circles in representing concepts in philosophy and mathematics. Playfair’s brother John was a highly regarded Enlightenment mathematician and scientist. It is likely that through John, William saw a divided circle used to display the component parts of a category. Mathematicians and philosophers used this type of illustration as far back as the 14th Century.
Today’s leading data visualization experts like Edward Tufte and Stephen Few dislike pie chart. Tufte writes that “A table is nearly always better than a dumb pie chart”, and in a long screed against the pie, Few admonishes data visualizers to “save the pies for dessert.”
Prediction, like medicine in the early 20th century, is still mostly based on eminence rather than evidence. The most famous forecasters in the world are newspaper columnists and television pundits. Superforecasters make for bad media stars. Caution, nuance and healthy scepticism are less telegenic than big hair, a dazzling smile and simplistic, confident pronouncements. But even if the hoped-for revolution never arrives, the techniques and habits of mind set out in this book are a gift to anyone who has to think about what the future might bring. In other words, to everyone.
→ The Economist
And my personal favorite :
→ Math With Bad Drawings
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