As a result, each market participant does not need to be a rocket scientist or have the ability to precisely measure tail risk to benefit from the better data made available under ultra transparency.
All each market participant needs to do to benefit from better data is to be able to recognize when the risk of an investment has increased and ask themselves: "am I comfortable with my current level of exposure given the increased risk of loss?"
For example, you don't need fancy statistical models or the ability to measure tail-risk to see that the risk of MF Global was increasing as the bet on sovereign debt increased. All that was necessary was to look at which sovereign debt was involved, the size of the position and compare it to book capital.
Knowing that the risk of loss was increasing provides the information necessary for a participant to rethink and adjust the size of their investment. This is why it is hard to object to better data.
Knowing that they are responsible for their losses gives market participants the incentive to act when they see the risk of loss increasing.
George Akerlof won a Noble Prize for a variant of this observation with his analysis of accounting control fraud. In the absence of better data, bank management can increase its compensation by increasing the risk of the bank.
This works because the absence of better data breaks the feedback loop where the increased risk would be offset by investors reducing their investments and increasing the bank's cost of funds to reflect the higher level of risk.
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