Why leveraged ETFs must lose money

Let f(x)f(x) be the price curve of an asset over time, and suppose we want to create a fund that offers returns twice that of ff. Precisely, we want our price curve to be

12f(x)2\frac12 f(x)^2

(why the factor of 12\frac12? it will make some later equations easier to read)

Can we achieve this? If ff is differentiable, the answer is yes. Let g(x)g(x) be the price curve of our fund. We set our position to f(x)f(x) so that

g′(x)=f(x)f′(x)g'(x) = f(x)f'(x)

which we can solve to get the desired return profile.

What if ff is not differentiable, but rather a Wiener process? We cannot differentiate with respect to time, so instead we look at how gg must change with little changes in ff. If we write g(x+ϵ)−g(x)g(x+\epsilon)-g(x) as Δg\Delta g, we get

Δg=f(x)(Δf)+12(Δf)2\Delta g = f(x)(\Delta f)+\frac12(\Delta f)^2

We can set our position to f(x)f(x) to achieve the left term, but what about the term on the right? When ff was differentiable this term vanished, but now in fact E⁡[(Δf)2]=ϵ\operatorname{E}[(\Delta f)^2] = \epsilon. So, in order to achieve the right term, our fund must increase in price by 12ϵ\frac12\epsilon every ϵ\epsilon. Assuming we cannot print money, we must instead allow our 2x leveraged fund to have the return profile

12f(x)2−12x\frac12 f(x)^2-\frac12 x

(why can we substitute E⁡[(Δf)2]\operatorname{E}[(\Delta f)^2] for (Δf)2(\Delta f)^2 and ignore the noise component (Δf)2−E⁡[(Δf)2](\Delta f)^2 - \operatorname{E}[(\Delta f)^2]? hint: calculate the variance in the accumulated noise)