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updated montecarlo methods
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victorballester7 committed Oct 19, 2023
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$$
This quantity can be larger or smaller than $\Var(Y )$ depending on the choice of $L$ and thus the success of importance sampling relies on the choice of an effective change of probability measure.
\end{remark}
\subsection{Simulation of diffusion processes}
The aim of this section is to develop methods to simulate solutions to SDEs of the form:
\begin{equation}\label{MM:SDE}
\begin{cases}
\dd{\vf{X}_t}=\vf{b}(\vf{X}_t)\dd{t}+\vf{\sigma}(\vf{X}_t)\dd{\vf{B}_t}
\vf{X}_0=\vf{x}_0
\end{cases}
\end{equation}
where $\vf{b}:\RR^d\to\RR^d$ and $\vf{\sigma}:\RR^d\to\mathcal{M}_{d\times d}(\RR)$ are Lipschitz continuous.
\subsubsection{Exact simulation}
\begin{proposition}
To simulate a sample $(\vf{B}_{t_1},\dots,\vf{B}_{t_n})$ of a $d$-dimensional Brownian motions, we can use the following algorithm:
\begin{enumerate}
\item Generate $(\vf{Z}_1,\dots,\vf{Z}_n)$ \iid $N_d(0,\vf{I}_d)$.
\item Set $\vf{B}_{t_0}:=0$ and for all $0\leq i\leq n-1$, set:
$$
\vf{B}_{t_{i+1}}=\vf{B}_{t_i}+\sqrt{t_{i+1}-t_i}\vf{Z}_{i+1}
$$
\end{enumerate}
\end{proposition}
\subsubsection{Euler scheme}
\begin{definition}
Consider the SDE of \mcref{MM:SDE} and let $h$ be a discretization step. The Euler method consists in:
\begin{align*}
\vf{X}_{t+h} & =\vf{X}_t+\int_t^{t+h}\vf{b}(\vf{X}_s)\dd{s}+\int_t^{t+h}\vf{\sigma}(\vf{X}_s)\dd{\vf{B}_s} \\
& \approx \vf{X}_t+h\vf{b}(\vf{X}_t)+\vf{\sigma}(\vf{X}_t)(\vf{B}_{t+h}-\vf{B}_t)
\end{align*}
More generally, if we want to obtain the solution at $(t_1,\dots,t_n)$, we can use the following algorithm. Set $\vf{\tilde{X}}_0:=\vf{x}_0$ and for all $0\leq i\leq n-1$, set:
$$
\vf{\tilde{X}}_{t_{i+1}}=\vf{\tilde{X}}_{t_i}+(t_{i+1}-t_i)\vf{b}(\vf{\tilde{X}}_{t_i})+\vf{\sigma}(\vf{\tilde{X}}_{t_i})(\vf{B}_{t_{i+1}}-\vf{B}_{t_i})
$$
\end{definition}
\begin{remark}
Note that Euler scheme reduces to generating independent increments $\vf{B}_{t_{i+1}}-\vf{B}_{t_i}\sim \sqrt{t_{i+1}-t_i}N_d(0,\vf{I}_d)$.
\end{remark}
\end{multicols}
\end{document}

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