# Proving $E[X^4]=3σ^4$

Given a random variable $X\sim\mathcal N(0,\sigma^2)$, how can we prove that $E[X^4]=3\sigma^4$? I am having trouble even starting with the proof.

• Hint: $\int x^4p(x)dx$. Integrate by parts. – user65203 Sep 7 '16 at 8:38
• ... and then do partial integration a couple times :) – b00n heT Sep 7 '16 at 8:39
• Or compute a bunch of derivatives of the moment generating function of $X$. – Surb Sep 7 '16 at 8:44
• Hint: let $X=\sigma U$ where $U$ has standard normal distribution. Then $\mathbb EX^4=\mathbb E\sigma^4U^4=\sigma^4\mathbb EU^4$. It remains to prove that $\mathbb EU^4=3$. For this see the other hints. If in calculations parameters can be avoided then do so. – drhab Sep 7 '16 at 8:44
• 1) Using the following web keywords "fourth order moment normal distribution proof", I have obtained at once for example this 2) The technical name for the fourth order moment is "kurtosis". – Jean Marie Sep 7 '16 at 9:07

First with $$\sigma=1$$, omitting the range $$(-\infty,\infty)$$ for convenience and integrating twice by parts

$$E[X^4]=\frac{\displaystyle\int x^4e^{-x^2/2}dx}{\displaystyle\int e^{-x^2/2}dx}=\frac{-x^3e^{-x^2/2}+3\displaystyle\int x^2e^{-x^2/2}dx}{\displaystyle\int e^{-x^2/2}dx}=\frac{0-3xe^{-x^2/2}+3\displaystyle\int e^{-x^2/2}dx}{\displaystyle\int e^{-x^2/2}dx}=3.$$

Then by rescaling the variable,

$$3\sigma^4.$$

By observing the pattern, you easily generalize to

$$E[X^{2n}]=(2n-1)!!\sigma^{2n}.$$

I list some hints below.

The probability density function of a normally distributed random variable with mean $0$ and variance $\sigma^2$ is

$$f(x) = \frac{1}{\sqrt{2 \pi \sigma^2}} \mathrm{e}^{-\frac{x^2}{2\sigma^2}}.$$

In general, you compute an expectation of a continuous random variable as

$$\mathbb{E}[g(X)] = \int_{-\infty}^\infty g(x) f(x) \, \mathrm{d}x.$$

For your particular question we have that $g(x) = x^4$ and therefore

$$\mathbb{E}[X^4] = \int_{-\infty}^\infty x^4 f(x) \, \mathrm{d}x = \frac{1}{\sqrt{2 \pi \sigma^2}} \int_{-\infty}^\infty x^4 \mathrm{e}^{-\frac{x^2}{2\sigma^2}} \, \mathrm{d}x.$$

You can solve this integral by using partial integration a number of times.

An alternative approach is to determine the moment generating function and differentiate. The moment generating function of a continuous random variable $X$ is defined as

$$M_X(t) := \mathbb{E}[\mathrm{e}^{tX}] = \int_{-\infty}^\infty \mathrm{e}^{t x} f(x) \, \mathrm{d}x, \quad t \in \mathbb{R}.$$

For your random variable $X$ we have

$$M_X(t) = \frac{1}{\sqrt{2 \pi \sigma^2}} \int_{-\infty}^\infty \mathrm{e}^{t x} \mathrm{e}^{-\frac{x^2}{2\sigma^2}} \, \mathrm{d}x.$$

Conveniently

$$\mathbb{E}[X^n] = \frac{\mathrm{d}^n}{\mathrm{d}t^n} M_X(t) \bigg\vert_{t = 0}.$$

• When using the PDF with $g(x) = x^4$, at what point does the $\frac{1}{\sqrt{2 \pi \sigma^2}}$ term disappear? – M Smith Apr 18 '18 at 13:33

The moment generating function of the standard normal $$M_{Z}(t)= e^{t^2/2}$$ has fouth derivative $$M_{Z}''''(t)=3M_{Z}''(t)+tM_{Z}'''(t)$$ Setting $$t=0$$ results in $$E(Z^4) = 3$$. Now, $$E(X^4)=\sigma^4E(Z^4)=3\sigma^4$$

@Yves Daoust already has a great answer, but we can omit a round of integration by parts, by just working it out in the most straight-forward way.

Applying integration by parts once, $$\mathbb{E} (x^4) = \int x^4 \varphi(x) dx = 0 + 3\sigma^2 \int x^2 \varphi(x) dx$$

where $$\varphi(x)$$ is normal PDF and $$\varphi(x) = \frac{1}{\sqrt{2\pi}\sigma} e^{-\frac{x^2}{2\sigma^2}}$$

We recognise $$\int x^2 \varphi(x) dx = \sigma^2$$. So,

$$\mathbb{E} (x^4) = 3\sigma^2 \sigma^2 = 3 \sigma^4$$