I'm quite new to the field so please bare with me.
Problem: Let ξ be a random variable distributed according to a log-normal distribution with parameters μ and $σ^2$, i.e. log(ξ) is normally distributed with mean μ and variance $σ^2$. Show that the kth moment of ξ is given by $E[ξ^k]=e^{kμ+(k^2σ^2)/2}$
Question: I suppose for this problem we can use the pdf of log-normal distribution and by the definition of the nth moment we need to take an integral $\int_{-\infty}^{\infty}x^nf(x)dx$ where f(x) is our pdf. However what is our x?
P.S.: I am confused by the log(ξ), it seems that we needed to kinda switch the representation of x to log(x) and then use the pdf of normal distribution to obtain a solution for random variable y, which corresponds to y=log(x). Once we get that we can simply find x=exp(y). Or is it just a different approach and we can boldly use log-normal distribution?
First of all observe that the moment generating function of a Gaussian random variable $X$ is: $$ M(t)=\mathbb{E}(e^{tX})=e^{\mu t+\frac 12\sigma^2 t^2}. $$ Given that $\log\xi$ is normally distributed, we can see: $$ \mathbb E(\xi^n)=\mathbb E(e^{n\log\xi})=M(n)=e^{\mu n+\frac 12\sigma^2 n^2}. $$
To explicitly answer your question, to find $n$-th moment of a random variable $\xi$, means to find $\mathbb E(\xi^n)$. Therefore if $\xi$ is a log-normal RV, the $n$-th moment is as follows: $$ \mathbb E(\xi^n)=\int_0^\infty \xi^n\frac{1}{\xi\sqrt{2\pi\sigma^2}}e^{-\frac{(\log\xi-\mu)^2}{2\sigma^2}}d\xi. $$