# Bounded moments for solution of stochastic differential equation

Consider the following SDE: $$\mathrm{d} X_t = - \lambda X_t + \mathrm{d} B_t$$ with initial condition $$X_0 = x$$, and where $$B_t$$ is a standard Brownian motion.

An application of Ito's formula gives us that the solution can be expressed as:

$$X_t = e^{- \lambda t} x + \int_0^t e^{- \lambda (t - s)} \mathrm{d} W_s$$

Is there an obvious way to check, for a given $$p$$, whether or not $$\mathrm{sup}_t\, \mathbb{E} |X_t|^p < \infty$$ ?

Since $$\int_0^t e^{-\lambda (t-s)} \, dW_s$$ is Gaussian (as a stochastic integral of a deterministic function with respect to Brownian motion), it follows that $$X_t$$ is Gaussian for each $$t$$. Its mean is given by

$$m_t := \mathbb{E}(X_t)= e^{-\lambda t} x + \underbrace{\mathbb{E} \left( \int_0^t e^{-\lambda(t-s)} \, dW_s \right)}_{0} = e^{-\lambda t} x$$

and the variance equals, by Itô's isometry,

\begin{align*} \sigma_t^2 := \mathbb{E}((X_t-m_t)^2) &= \mathbb{E} \left( \left| \int_0^te^{-\lambda (t-s)} \, dW_s \right|^2 \right) \\&= \int_0^t e^{-2\lambda (t-s)} \, ds \\ &= \int_0^t e^{-2 \lambda s} \, ds \\ &= \frac{1-e^{-2\lambda t}}{2\lambda}. \end{align*}

Consequently, we have $$X_t \stackrel{d}{=} m_t + \sigma_t U$$ for $$U \sim N(0,1)$$. Using that $$|x+y|^p \leq c_p |x|^p + c_p |y|^p$$ for some constant $$c_p>0$$, it follows that

$$\mathbb{E}(|X_t|^p) \leq c_p |m_t|^p + c_p \sigma_t^p \mathbb{E}(|U|^p).$$

This means that $$\sup_{t \geq 0} \mathbb{E}(|X_t|^p) < \infty$$ if $$\sup_{t \geq 0} |m_t|^p + \sup_{t \geq 0} \sigma_t^p < \infty. \tag{1}$$

As $$\sigma_t \leq 1/\sqrt{2\lambda}$$ and $$|m_t| \leq |x|$$ for any $$t>0$$, we find that $$(1)$$ holds for any $$p>0$$. Hence, $$\sup_{t \geq 0} \mathbb{E}(|X_t|^p)< \infty \quad \text{for all p>0.}$$