Given iid random variables $X_1, \dots, X_n$ with common density: $$ f(x) = 1\{ x > 0 \} \cdot \frac{1}{(x+1)^2} $$ it is supposed to be the case that $\frac{\max_i X_i}{n}$ converges in distribution.
Question: How can these random variables converge in distribution if the pointwise limit of their cumulative distribution functions isn't even an increasing function?
Details: I thought this was going to work out nicely and that the sequence would converge in distribution cleanly to an exponential random variable.
The CDF of each $X_i$ is $1\{x >0 \} (1 - \frac{1}{1+x})$, so since the $X_i$ are iid, the CDF of their maximum should just be the product of their CDF's, i.e. $1\{x >0 \} \left(1 - \frac{1}{1+x}\right)^n$.
Presumably, $P(\frac{\max_i X_i}{n} \le x) = P(\max_i X_i \le nx)$, so that we get for the CDF's of the relevant sequence of random variables:
$$P\left(\frac{\max_i X_i}{n} \le x\right) = \begin{cases}0 & x \le 0 \\ \left(1-\frac{1}{1+nx}\right)^n & x > 0 \end{cases}$$
Taking the limit as $n \to \infty$, this approaches $1\{x > 0 \} e^{-x}$, which isn't even an increasing function, and isn't the CDF of an exponential, $1\{x > 0 \}(1 - e^{-x})$.
Is the claim I am trying to prove wrong? Are my calculations wrong? Or is my understanding of convergence in distribution wrong? Or something else?