Reflection principle (Wiener process)

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Simulation of Wiener process (black curve). When the process reaches the crossing point at a=50 at t3000, both the original process and its reflection (red curve) about the a=50 line (blue line) are shown. After the crossing point, both black and red curves have the same distribution.

In the theory of probability for stochastic processes, the reflection principle for a Wiener process states that if the path of a Wiener process f(t) reaches a value f(s) = a at time t = s, then the subsequent path after time s has the same distribution as the reflection of the subsequent path about the value a.[1] More formally, the reflection principle refers to a lemma concerning the distribution of the supremum of the Wiener process, or Brownian motion. The result relates the distribution of the supremum of Brownian motion up to time t to the distribution of the process at time t. It is a corollary of the strong Markov property of Brownian motion.


If is a Wiener process, and is a threshold (also called a crossing point), then the lemma states:

In a stronger form, the reflection principle says that if is a stopping time then the reflection of the Wiener process starting at , denoted , is also a Wiener process, where:

and the indicator function and is defined similarly. The stronger form implies the original lemma by choosing .


The earliest stopping time for reaching crossing point a, , is an almost surely bounded stopping time. Then we can apply the strong Markov property to deduce that a relative path subsequent to , given by , is also simple Brownian motion independent of . Then the probability distribution for the last time is at or above the threshold in the time interval can be decomposed as


By the tower property for conditional expectations, the second term reduces to:

since is a standard Brownian motion independent of and has probability of being less than . The proof of the lemma is completed by substituting this into the second line of the first equation.[2]



The reflection principle is often used to simplify distributional properties of Brownian motion. Considering Brownian motion on the restricted interval then the reflection principle allows us to prove that the location of the maxima , satisfying , has the arcsine distribution. This is one of the Lévy arcsine laws.[3]


  1. ^ Jacobs, Kurt (2010). Stochastic Processes for Physicists. Cambridge University Press. pp. 57–59. ISBN 9781139486798.
  2. ^ Mörters, P.; Peres,Y. (2010) Brownian Motion, CUP. ISBN 978-0-521-76018-8
  3. ^ Lévy, Paul (1940). "Sur certains processus stochastiques homogènes". Compositio Mathematica. 7: 283–339. Retrieved 15 February 2013.