Fractals , JP Morgan; how a CIO can become the scapegoat.

JP Morgan Chase many billion loss (some say $2B, some say $4B) is a big mess, but what is relevant, the CIO is the first to be beheaded. This is stupid.

Quote:

The loss reportedly came within the last six weeks and resulted from trading in so-called credit derivatives. By the closing of the markets on Friday, roughly 10 percent of the firm’s stock price was lopped off.  JPMorgan Chase is the largest bank in the Unites States.

This is no news per-se. Derivatives are artificial paper created by computer via big data analytic and cloud data collections. What is new is to punish the CIO and not the rogue traders. The most famous rogue trader is Nick Leeson, who was a derivatives trader at the Singapore office of Britain's Barings Bank., which went bankrupt for a mere $1B loss.

But the CIO?  Is there such thing as "rogue CIO"? No. But there is such thing as the Human Factor.

Some people, in addition to software should check key decisions. Cloud Sprawl can be as lethal, as it can be a blessing. One can not replace a human trader with a computer doing automated trades, no matter how successful these programs were in the past. The future, say a branch of mathematics called "fractals",  can not be predicted.

Benoit Mandelbrot 1924 - 2010

This is what the late Benoit Mandelbrot the creator of multi-fractals, wrote in Scientific American How Fractals Can Explain What's Wrong with Wall Street in a  2008 in the article :

The discrepancies between the pictures painted by modern portfolio theory and the actual movement of prices are obvious. Prices do not vary continuously, and they oscillate wildly at all timescales. Volatility—far from a static entity to be ignored or easily compensated for—is at the very heart of what goes on in financial markets. In the past, money managers embraced the continuity and constrained price movements of modern portfolio theory because of the absence of strong alternatives. But a money manager need no longer accept the current financial models at face value.

Instead multifractals can be put to work to “stress-test” a portfolio. In this technique the rules underlying multifractals attempt to create the same patterns of variability as do the unknown rules that govern actual markets. Multifractals describe accurately the relation between the shape of the generator and the patterns of up-and-down swings of prices to be found on charts of real market data.

The financial models can  not provide the exact day and price of derivative.

On a practical level, this finding suggests that a fractal generator can be developed based on historical market data. The actual model used does not simply inspect what the market did yesterday or last week. It is in fact a more realistic depiction of market fluctuations, called fractional Brownian motion in multifractal trading time. The charts created from the generators produced by this model can simulate alternative scenarios based on previous market activity.

These techniques do not come closer to forecasting a price drop or rise on a specific day on the basis of past records. But they provide estimates of the probability of what the market might do and allow one to prepare for inevitable sea changes. The new modeling techniques are designed to cast a light of order into the seemingly impenetrable thicket of the financial markets. They also recognize the mariner’s warning that, as recent events demonstrate, deserves to be heeded: On even the calmest sea, a gale may be just over the horizon.

 This is an insightful quote In Plato's Cave article in Economist:

... banks have made of “value-at-risk” (VAR) calculations, a measure of the potential losses of a portfolio. This is supposed to show whether banks and other financial outfits are being safely run. Regulators use VAR calculations to work out how much capital banks need to put aside for a rainy day. But the calculations are flawed.

The mistake was to turn a blind eye to what is known as “tail risk”. Think of the banks’ range of possible daily losses and gains as a distribution. Most of the time you gain a little or lose a little. Occasionally you gain or lose a lot. Very rarely you win or lose a fortune. If you plot these daily movements on a graph, you get the familiar bell-shaped curve of a normal distribution (see chart 4). Typically, a VAR calculation cuts the line at, say, 98% or 99%, and takes that as its measure of extreme losses.

The last paragraph ends with a quote from Edmund Phelps, 2006 Nobel prize in economics

“Risk-assessment and risk-management models were never well founded,” he says. “There was a mystique to the idea that market participants knew the price to put on this or that risk. But it is impossible to imagine that such a complex system could be understood in such detail and with such amazing correctness…the requirements for information…have gone beyond our abilities to gather it.”

Every trading strategy draws upon a model, even if it is not expressed in mathematical symbols. But Mr Phelps believes that mathematics can take you only so far. There is a big role for judgment and intuition, things that managers are supposed to provide. Why have they failed?