Quote from TraderZones:
Before making wild assumptions about what caused the crash of 1987, you need to have a basic grasp of economics.
As I recall, interest rates were doing wild things that year prior to the crash, that precipitated it. I don't have the 87 bond or index charts up, but I believe that was the year that happened. It was not a surprise at all, and had little to do with quants or mathematicians.
I made $5K that day, by loading up on mutual funds at EOD prices. I figured it was a serious overreaction, and I cashed in.
But your analysis is seriously weak. As I said before, unsupported opinions are fertilizer.
I don't disagree with you in terms of the trigger.
Portfolio insurance dictated that you sell your positions as the prices fall - of course it's more complex than that. In 1987, the market fell 23% in one day. Of course, there had to be a trigger for people to initiate their portfolio insurance trades.
The problem obviously is that people leant on the portfolio insurance as it effectively told them that they had little risk because they could simply issue the relevant sell orders as prices fell and they would be well covered.
What actually happened of course is that everyone sold at the same time, prices fell and portfolio insurance dictated that they sell again and again and again. There you have excessive risk combined with a model that told everyone to sell at the same time with the obvious consequences.
Similarly of late - there could have been a lot of Puerto-Rican gardeners with $14K salaries ad $1.5 Million mortgages default and the world would barely have noticed. At the heart of this crisis was the Gaussian Copula formula.
Gaussian Copula was a clever bit of math which claimed to model default correlation without looking at historical data but instead used current market data from credit default swaps. So - instead of using historical data (also inherently flawed), the correlated risk of mortgage defaults was calculated based on the price of another instrument. In terms of correlated risk - I'm going to borrow from an article published by a friend :
To understand the mathematics of correlation better, Salmon said that we must consider something simple, like a kid in an elementary school: Let's call her Alice, he said.
âThe 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 oneânot 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 percentâwhich 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 probabilitiesâthe chance that Alice will get head lice if Britney gets head liceâis 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.â
So - Gaussian Copula gave an analysis of risk that was unrealistic. Our banks used that forumla to take massive risks they didn't really understand on very illiquid products. In fact, the banks probably didn't even understand the massive risk because it was the Gaussian Copula that was used to rate the derivatives products triple A when they were junk.
Sure - Pablo's mortgage default may have been the last straw BUT it was the quantitative mathematics and the consequent massive risk taking that nearly brought down the financial markets.
