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  "body": "\n## Definition\n\nLet $(\\Omega, \\mathcal{F}, (\\mathcal{F}_t)_{t \\in T}, P)$ be a probability space with a [[filtration|HNK1Y62G7V]], where $T \\subseteq [0, \\infty)$.\nLet $X = (X_t)_{t \\in T}$ be a [[stochastic process|0QH5ENKZM1]] with values in a measurable space $S$, and let $\\tau$ be a [[stopping time|WND87FBAKC]].\nThe *stopped process* $X^\\tau = (X^\\tau_t)_{t \\in T}$ is defined by\n$$X^\\tau_t(\\omega) = X_{\\min(t, \\tau(\\omega))}(\\omega) \\quad \\text{for } t \\in T \\text{ and } \\omega \\in \\Omega.$$\n\n## Remarks\n\nThe notation $t \\wedge \\tau = \\min(t, \\tau)$ is standard, so $X^\\tau_t = X_{t \\wedge \\tau}$.\nOn the event $\\set{\\tau = \\infty}$, the stopped process equals $X$.\n\nIn discrete time, if $X$ is [[adapted|YRNFE016FF]], then so is $X^\\tau$, because\n$$X^\\tau_n = X_0 + \\sum_{k=1}^{n} \\mathbf{1}_{\\set{\\tau \\ge k}} (X_k - X_{k-1})$$\nfor real-valued $X$, and $\\set{\\tau \\ge k} = \\Omega \\setminus \\set{\\tau \\le k - 1} \\in \\mathcal{F}_{k-1}$.\nThe same formula shows that a stopped [[martingale|0D0GBNDKDH]] is a martingale.\nIn continuous time, the stopped process of a progressively measurable process is progressively measurable.\n\nExample: let $S$ be a simple symmetric [[random walk|04VZE3FZXF]] started at $0$ and let $\\tau$ be the first time with $\\abs{S_n} = 5$.\nThen $\\abs{S^\\tau_n} \\le 5$ for all $n$, so $S^\\tau$ is a bounded martingale.\n\nNon-example: if $\\tau$ is not a stopping time, the formula still defines a process, but it need not be adapted.\nTake the walk above and let $\\sigma$ be the last visit to $0$ before time $10$.\nThe value $S^\\sigma_1$ equals $S_1$ if the walk returns to $0$ between times $1$ and $10$, and $0$ otherwise.\nThis is not determined by $\\mathcal{F}_1$, so $S^\\sigma$ is not adapted.\n",
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