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jamez1

Paper available here:

https://people.csail.mit.edu/ghobadi/papers/teavar_sigcomm_2...

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It just appears to apply VaR to failure rates, which is hardly a 'wall street secret'.

The authors also completely misunderstand how VaR works - it is the minimum value at risk, not the maximum.

Value at Risk (VaR) [33] captures precisely these bounds. Given a probability threshold β (say β = 0.99), VaRβ provides a probabilistic upper bound on the loss: the loss is less than VaRβ with probability β

It is actually a probabilistic lower bound on the loss.

Value at risk is a probabilistic upper bound on the loss. See for example:

https://www.risk.net/definition/value-at-risk-var

"It is defined as the maximum dollar amount expected to be lost over a given time horizon, at a pre-defined confidence level. For example, if the 95% one-month VAR is $1 million, there is 95% confidence that over the next month the portfolio will not lose more than $1 million."

That is incorrect, most definitions you'll see of VaR are incorrect. Think about it logically - the purpose is to quantify the tail events you've observed in the past. Practically that would be the lower bound (as the future can surprise you more).

Read the discussion on VaR here if you're interested in the detail: https://martinkronicle.com/aaron-browns-red-blooded-risk/

I'm not sure what you're claiming here: that the authors used the wrong definition for VaR, or that they misread their own definition. Either way, they look correct to me. VaR is a probabilistic upper bound on the loss: You can be 99% sure the loss is no greater than VaRβ, though it may be much less or (in the finance case) negative.

I understand if I am correct, probabilistic lower bound with 0.01 probability, upper bound to the loss with 0.99 probability.

network operators already use a sort of var strategy — Where there’s actual value at risk, the value customers pre-empt general bandwidth in the case of an outage. That’s kind of the TE principle the authors were claiming to improve.

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