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Algorithmic Trading Strategies Are Short the Volatility of Volatility in the Short Run, but Long the Volatility of Volatility in the Long Run

I think this from the thoughtful and intelligent Emanuel Derman is wrong:

Emanuel Derman's Blog: Algorithmic Trading Strategies: It always seemed to me, and recent occurences seem to confirm it, that most algorithmic trading strategies are long volatility but short volatility of volatility...

It seems to me that this is probably wrong in a subtle fashion. When volatility declines, the value of the current positions held by a smart algorithmic trading strategy are likely to rise--it is going to report gangbusting profits in its current accounting period. But the decline in volatility means that it has little opportunity to exploit mispricings now and in the future. So when volatility declines funds pursuing smart algorithmic trading strategies are worth less of a premium going forward. So a fall in volatility should lead them to (a) report large profits, but (b) cut their fees because they can offer less value-added in the future, and (c) reduce their scope of operations.

By contrast, a rise in volatility sees funds pursuing smart algorithmic trading strategies get absolutely hammered. But they have great opportunities going forward.

Hence we right now have the interesting spectacle of people saying today: (a) we lost half our clients' money, but (b) our strategies are sound and (c) are opportunities going forward are unbelievable, so (d) you should invest and (e) we should raise our fees because we can offer more value added, and (f) we are expanding our operations.

The problem of course, is that when you have just lost half your clients' money it takes either an incredibly sophisticated or an incredibly unsophisticated investor to take that as a sign of your fundamental excellence. See, once again, Shleifer and Vishny. See also John Meriwether, trying to make these points to his investors in the LTCM context in 1998: