He was very dignified and helpful to some of the mathematicians working with us. Ultimately fractals and related processes such as volatility are nice ways of measuring process outcomes, but they tell one nothing about input processes for controlling risk.

Critical instability in systems and power law related extreme outcomes are common. If anyone is interested in speaking about these things drop me a line http://www.gogerty.com

The flash crash was a large event from the HFT process. there have been 20 smaller crashes since then indicating the process is still unfolding and likely to exhibit a large outcome at some point in time, similar or greater than the flash crash of may 6, 2010.

The example I gave earlier was AIGFP notorious swaps which people seem to think started paying out because the reference obligations started to have credit events. Actually in most cases – possibly all – this simply wasn’t the case. What happened is that the market **PRICES** of the reference obligations declined triggering collateral payments for the swaps which then grew to a size that AIGFP got credit downgraded which triggered more collateral payments etc. Arguably the quant models were correct in valuing the swaps – and also irrelevent – and the issue was liquidity management.

In any case I don’t see how any of that affects my critique. Granted, I don’t think I voiced that critique very clearly or succinctly, and the point itself moved around a little, so let’s try again.

The complexity and opacity of quant methods as a whole, combined with a general track record of good short-term profitability, interacts with human nature to create a general tendency toward complacency about a financial firm’s capacity to handle potential problems in that market.

As a side note, if you want to convert volatility to price, you’ve got to use a Gaussian model to do it. It’s the only way. And I’m sure that’s fine, especially if you’re the only one doing it. But if everyone’s doing it in a particular market, using similar methods and the same volatility data, I’d be concerned with collective action problems. Because it seems to me you’d be inviting automatic feedback loops of artificial agreement (“creating consistency”?) where less agreement would exist otherwise, and and over time that would create distorted market data, worsening the problem. It may create actionable intelligence at the expense of unbiased intelligence, in other words. And when the bias is geared toward short-term contacts and profits, it seems to me that long-term bubble propensity in the markets should increase as a result. But that’s just remote pontification.

Ultimately I think the main cause of the crisis had more to do with behavioral factors related to quant models rather than the models themselves.

If you disagree, please let me know why, because that’s the alternative explanation of the crisis I’ve been using to defend traders from the critics who think they’re all bloodsucking dee-bags who sank the economy for the sake of a bonus bump. I prefer to think that financial types simply overestimated their predictive powers and failed to save money in the good times to protect them from the bad, just like the rest of us.

The number one use of B-S is actually what Danny_Black said: converting a quoted vol into a dollar price, which kind of important to know. This quoting convention is not arbitrary; for some markets it is much more convenient (stable) to quote in moneyness/vol terms than strike/price. When it is used for pricing, it is to create consistency in a set of related quotes. Nobody is building up a huge gamma/vega position and just delta hedging it without thinking.

But I do suspect that quants have spent a little too much time learning complex methods and not enough time understanding the fragile philosophy behind the methods. As a social scientist I have to constantly question my assumptions and anticipate all the ways in which my models could break. And they almost always break in practice. Markets are also a chaotic social phenomenon and in my opinion, deserve the same degree of statistical hesitance. But the quant strategies employed in markets seem a little too focused on firm-level results and miss the collective market-level impact of their behavior. Too many trees, not enough forest.

First, Mandlebrot didn’t give much aid to the field of practical quant finance because he doesn’t believe in it, not because he “wasn’t troubled” to do so.

And the problem lies with the point you’re both making: that B-S and similar strategies work very well when the market isn’t broken. Options, for example, are typically short-term instruments and the distribution of the underlying really doesn’t matter much in the context of a single trade since the distribution will be narrow regardless. And as long as the market behaves itself, it’s going to give you something close to a perfectly hedged investment strategy.

But in the medium run the market doesn’t behave itself. Correlations suddenly move toward 1 and the models break. And because most of the financial sector now relies on algorithmic trading strategies, there’s a serious risk that at least some systemically-important institutions will fall into sleepy optimism by fat profits and spreadsheets that say they’re perfectly hedged, and overextend themselves in the run-up.

So the problem isn’t that the models don’t work. It’s that they work too well most of the time. The drawback is that it’s impossible to quantify how much you can continue to rely on them during a large unexpected change in market behavior (that’s where the inflated variance of the underlying distribution matters – these events happen often and have huge effects) and their short term profit-maximizing features virtually guarantee over-reliance across the industry. That breeds both a tendency toward systemic weakness and a deeply inefficient first response.

quantacide, please name me one bank that uses CAPM in any significant way for interal risk management so i can put all of my wealth into shorting it.

Not entirely sure what a Pareto distribution fractal algorithm means but I know of plenty of people who tried to apply different forms of chaos or complexity theory with very mixed results. Certainly they made less money less consistently than the toolkits you seem to be criticising.

I am obviously not saying that quant models today have reached complete perfection any more than say the Standard Model in particle physics has done but I was making a comment that what journalists seem to think banks use and what they actually use seem to have zero correlation. Far worse is this idea that somehow banks are **deliberately** looking to avoid having a good pricing model is simply insane.

There seems to be this narrative that somehow finance people have formed some sort of conspiracy to deny the True Financial Theory(TM) from replacing the guassian model whose sole purpose is to give traders money/extort money from the government. I can guarantee that if someone had anything that looked even vaguely like it could perform that he would have money throw at him.