Wiener process
In mathematics, the Wiener process is a realvalued continuoustime stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the onedimensional Brownian motion.^{[1]} It is often also called Brownian motion due to its historical connection with the physical process of the same name originally observed by Scottish botanist Robert Brown. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary independent increments) and occurs frequently in pure and applied mathematics, economics, quantitative finance, evolutionary biology, and physics.
The Wiener process plays an important role in both pure and applied mathematics. In pure mathematics, the Wiener process gave rise to the study of continuous time martingales. It is a key process in terms of which more complicated stochastic processes can be described. As such, it plays a vital role in stochastic calculus, diffusion processes and even potential theory. It is the driving process of Schramm–Loewner evolution. In applied mathematics, the Wiener process is used to represent the integral of a white noise Gaussian process, and so is useful as a model of noise in electronics engineering (see Brownian noise), instrument errors in filtering theory and disturbances in control theory.
The Wiener process has applications throughout the mathematical sciences. In physics it is used to study Brownian motion, the diffusion of minute particles suspended in fluid, and other types of diffusion via the Fokker–Planck and Langevin equations. It also forms the basis for the rigorous path integral formulation of quantum mechanics (by the Feynman–Kac formula, a solution to the Schrödinger equation can be represented in terms of the Wiener process) and the study of eternal inflation in physical cosmology. It is also prominent in the mathematical theory of finance, in particular the Black–Scholes option pricing model.
Characterisations of the Wiener process
The Wiener process [math]\displaystyle{ W_t }[/math] is characterised by the following properties:^{[2]}
 [math]\displaystyle{ W_0= 0 }[/math]
 [math]\displaystyle{ W }[/math] has independent increments: for every [math]\displaystyle{ t\gt 0, }[/math] the future increments [math]\displaystyle{ W_{t+u}  W_t, }[/math] [math]\displaystyle{ u \ge 0, }[/math] are independent of the past values [math]\displaystyle{ W_s }[/math], [math]\displaystyle{ s\lt t. }[/math]
 [math]\displaystyle{ W }[/math] has Gaussian increments: [math]\displaystyle{ W_{t+u}  W_t }[/math] is normally distributed with mean [math]\displaystyle{ 0 }[/math] and variance [math]\displaystyle{ u }[/math], [math]\displaystyle{ W_{t+u}  W_t\sim \mathcal N(0,u). }[/math]
 [math]\displaystyle{ W }[/math] has continuous paths: [math]\displaystyle{ W_t }[/math] is continuous in [math]\displaystyle{ t }[/math].
That the process has independent increments means that if 0 ≤ s_{1} < t_{1} ≤ s_{2} < t_{2} then W_{t1} − W_{s1} and W_{t2} − W_{s2} are independent random variables, and the similar condition holds for n increments.
An alternative characterisation of the Wiener process is the socalled Lévy characterisation that says that the Wiener process is an almost surely continuous martingale with W_{0} = 0 and quadratic variation [W_{t}, W_{t}] = t (which means that W_{t}^{2} − t is also a martingale).
A third characterisation is that the Wiener process has a spectral representation as a sine series whose coefficients are independent N(0, 1) random variables. This representation can be obtained using the Karhunen–Loève theorem.
Another characterisation of a Wiener process is the definite integral (from time zero to time t) of a zero mean, unit variance, delta correlated ("white") Gaussian process.^{[3]}
The Wiener process can be constructed as the scaling limit of a random walk, or other discretetime stochastic processes with stationary independent increments. This is known as Donsker's theorem. Like the random walk, the Wiener process is recurrent in one or two dimensions (meaning that it returns almost surely to any fixed neighborhood of the origin infinitely often) whereas it is not recurrent in dimensions three and higher.^{[4]} Unlike the random walk, it is scale invariant, meaning that [math]\displaystyle{ \alpha^{1} W_{\alpha^2 t} }[/math] is a Wiener process for any nonzero constant α. The Wiener measure is the probability law on the space of continuous functions g, with g(0) = 0, induced by the Wiener process. An integral based on Wiener measure may be called a Wiener integral.
Wiener process as a limit of random walk
Let [math]\displaystyle{ \xi_1, \xi_2, \ldots }[/math] be i.i.d. random variables with mean 0 and variance 1. For each n, define a continuous time stochastic process [math]\displaystyle{ W_n(t)=\frac{1}{\sqrt{n}}\sum\limits_{1\leq k\leq\lfloor nt\rfloor}\xi_k, \qquad t \in [0,1]. }[/math] This is a random step function. Increments of [math]\displaystyle{ W_n }[/math] are independent because the [math]\displaystyle{ \xi_k }[/math] are independent. For large n, [math]\displaystyle{ W_n(t)W_n(s) }[/math] is close to [math]\displaystyle{ N(0,ts) }[/math] by the central limit theorem. Donsker's theorem asserts that as [math]\displaystyle{ n \to \infty }[/math], [math]\displaystyle{ W_n }[/math] approaches a Wiener process, which explains the ubiquity of Brownian motion.^{[5]}
Properties of a onedimensional Wiener process
Basic properties
The unconditional probability density function follows a normal distribution with mean = 0 and variance = t, at a fixed time t: [math]\displaystyle{ f_{W_t}(x) = \frac{1}{\sqrt{2 \pi t}} e^{x^2/(2t)}. }[/math]
The expectation is zero: [math]\displaystyle{ \operatorname E[W_t] = 0. }[/math]
The variance, using the computational formula, is t: [math]\displaystyle{ \operatorname{Var}(W_t) = t. }[/math]
These results follow immediately from the definition that increments have a normal distribution, centered at zero. Thus [math]\displaystyle{ W_t = W_tW_0 \sim N(0,t). }[/math]
Covariance and correlation
The covariance and correlation (where [math]\displaystyle{ s \leq t }[/math]): [math]\displaystyle{ \begin{align} \operatorname{cov}(W_s, W_t) &= s, \\ \operatorname{corr}(W_s,W_t) &= \frac{\operatorname{cov}(W_s,W_t)}{\sigma_{W_s} \sigma_{W_t}} = \frac{s}{\sqrt{st}} = \sqrt{\frac{s}{t}}. \end{align} }[/math]
These results follow from the definition that nonoverlapping increments are independent, of which only the property that they are uncorrelated is used. Suppose that [math]\displaystyle{ t_1\leq t_2 }[/math]. [math]\displaystyle{ \operatorname{cov}(W_{t_1}, W_{t_2}) = \operatorname{E}\left[(W_{t_1}\operatorname{E}[W_{t_1}]) \cdot (W_{t_2}\operatorname{E}[W_{t_2}])\right] = \operatorname{E}\left[W_{t_1} \cdot W_{t_2} \right]. }[/math]
Substituting [math]\displaystyle{ W_{t_2} = ( W_{t_2}  W_{t_1} ) + W_{t_1} }[/math] we arrive at: [math]\displaystyle{ \begin{align} \operatorname{E}[W_{t_1} \cdot W_{t_2}] & = \operatorname{E}\left[W_{t_1} \cdot ((W_{t_2}  W_{t_1})+ W_{t_1}) \right] \\ & = \operatorname{E}\left[W_{t_1} \cdot (W_{t_2}  W_{t_1} )\right] + \operatorname{E}\left[ W_{t_1}^2 \right]. \end{align} }[/math]
Since [math]\displaystyle{ W_{t_1}=W_{t_1}  W_{t_0} }[/math] and [math]\displaystyle{ W_{t_2}  W_{t_1} }[/math] are independent, [math]\displaystyle{ \operatorname{E}\left [W_{t_1} \cdot (W_{t_2}  W_{t_1} ) \right ] = \operatorname{E}[W_{t_1}] \cdot \operatorname{E}[W_{t_2}  W_{t_1}] = 0. }[/math]
Thus [math]\displaystyle{ \operatorname{cov}(W_{t_1}, W_{t_2}) = \operatorname{E} \left [W_{t_1}^2 \right ] = t_1. }[/math]
A corollary useful for simulation is that we can write, for t_{1} < t_{2}: [math]\displaystyle{ W_{t_2} = W_{t_1}+\sqrt{t_2t_1}\cdot Z }[/math] where Z is an independent standard normal variable.
Wiener representation
Wiener (1923) also gave a representation of a Brownian path in terms of a random Fourier series. If [math]\displaystyle{ \xi_n }[/math] are independent Gaussian variables with mean zero and variance one, then [math]\displaystyle{ W_t = \xi_0 t+ \sqrt{2}\sum_{n=1}^\infty \xi_n\frac{\sin \pi n t}{\pi n} }[/math] and [math]\displaystyle{ W_t = \sqrt{2} \sum_{n=1}^\infty \xi_n \frac{\sin \left(\left(n  \frac{1}{2}\right) \pi t\right)}{ \left(n  \frac{1}{2}\right) \pi} }[/math] represent a Brownian motion on [math]\displaystyle{ [0,1] }[/math]. The scaled process [math]\displaystyle{ \sqrt{c}\, W\left(\frac{t}{c}\right) }[/math] is a Brownian motion on [math]\displaystyle{ [0,c] }[/math] (cf. Karhunen–Loève theorem).
Running maximum
The joint distribution of the running maximum [math]\displaystyle{ M_t = \max_{0 \leq s \leq t} W_s }[/math] and W_{t} is [math]\displaystyle{ f_{M_t,W_t}(m,w) = \frac{2(2m  w)}{t\sqrt{2 \pi t}} e^{\frac{(2mw)^2}{2t}}, \qquad m \ge 0, w \leq m. }[/math]
To get the unconditional distribution of [math]\displaystyle{ f_{M_t} }[/math], integrate over −∞ < w ≤ m: [math]\displaystyle{ \begin{align} f_{M_t}(m) & = \int_{\infty}^m f_{M_t,W_t}(m,w)\,dw = \int_{\infty}^m \frac{2(2m  w)}{t\sqrt{2 \pi t}} e^{\frac{(2mw)^2}{2t}} \,dw \\[5pt] & = \sqrt{\frac{2}{\pi t}}e^{\frac{m^2}{2t}}, \qquad m \ge 0, \end{align} }[/math]
the probability density function of a Halfnormal distribution. The expectation^{[6]} is [math]\displaystyle{ \operatorname{E}[M_t] = \int_0^\infty m f_{M_t}(m)\,dm = \int_0^\infty m \sqrt{\frac{2}{\pi t}}e^{\frac{m^2}{2t}}\,dm = \sqrt{\frac{2t}{\pi}} }[/math]
If at time [math]\displaystyle{ t }[/math] the Wiener process has a known value [math]\displaystyle{ W_{t} }[/math], it is possible to calculate the conditional probability distribution of the maximum in interval [math]\displaystyle{ [0, t] }[/math] (cf. Probability distribution of extreme points of a Wiener stochastic process). The cumulative probability distribution function of the maximum value, conditioned by the known value [math]\displaystyle{ W_t }[/math], is: [math]\displaystyle{ \, F_{M_{W_t}} (m) = \Pr \left( M_{W_t} = \max_{0 \leq s \leq t} W(s) \leq m \mid W(t) = W_t \right) = \ 1 \ e^{2\frac{m(m  W_t)}{t}}\ \, , \,\ \ m \gt \max(0,W_t) }[/math]
Selfsimilarity
Brownian scaling
For every c > 0 the process [math]\displaystyle{ V_t = (1 / \sqrt c) W_{ct} }[/math] is another Wiener process.
Time reversal
The process [math]\displaystyle{ V_t = W_1  W_{1t} }[/math] for 0 ≤ t ≤ 1 is distributed like W_{t} for 0 ≤ t ≤ 1.
Time inversion
The process [math]\displaystyle{ V_t = t W_{1/t} }[/math] is another Wiener process.
A class of Brownian martingales
If a polynomial p(x, t) satisfies the partial differential equation [math]\displaystyle{ \left( \frac{\partial}{\partial t} + \frac{1}{2} \frac{\partial^2}{\partial x^2} \right) p(x,t) = 0 }[/math] then the stochastic process [math]\displaystyle{ M_t = p ( W_t, t ) }[/math] is a martingale.
Example: [math]\displaystyle{ W_t^2  t }[/math] is a martingale, which shows that the quadratic variation of W on [0, t] is equal to t. It follows that the expected time of first exit of W from (−c, c) is equal to c^{2}.
More generally, for every polynomial p(x, t) the following stochastic process is a martingale: [math]\displaystyle{ M_t = p ( W_t, t )  \int_0^t a(W_s,s) \, \mathrm{d}s, }[/math] where a is the polynomial [math]\displaystyle{ a(x,t) = \left( \frac{\partial}{\partial t} + \frac 1 2 \frac{\partial^2}{\partial x^2} \right) p(x,t). }[/math]
Example: [math]\displaystyle{ p(x,t) = \left(x^2  t\right)^2, }[/math] [math]\displaystyle{ a(x,t) = 4x^2; }[/math] the process [math]\displaystyle{ \left(W_t^2  t\right)^2  4 \int_0^t W_s^2 \, \mathrm{d}s }[/math] is a martingale, which shows that the quadratic variation of the martingale [math]\displaystyle{ W_t^2  t }[/math] on [0, t] is equal to [math]\displaystyle{ 4 \int_0^t W_s^2 \, \mathrm{d}s. }[/math]
About functions p(xa, t) more general than polynomials, see local martingales.
Some properties of sample paths
The set of all functions w with these properties is of full Wiener measure. That is, a path (sample function) of the Wiener process has all these properties almost surely.
Qualitative properties
 For every ε > 0, the function w takes both (strictly) positive and (strictly) negative values on (0, ε).
 The function w is continuous everywhere but differentiable nowhere (like the Weierstrass function).
 Points of local maximum of the function w are a dense countable set; the maximum values are pairwise different; each local maximum is sharp in the following sense: if w has a local maximum at t then [math]\displaystyle{ \lim_{s \to t} \frac{w(s)w(t)}{st} \to \infty. }[/math] The same holds for local minima.
 The function w has no points of local increase, that is, no t > 0 satisfies the following for some ε in (0, t): first, w(s) ≤ w(t) for all s in (t − ε, t), and second, w(s) ≥ w(t) for all s in (t, t + ε). (Local increase is a weaker condition than that w is increasing on (t − ε, t + ε).) The same holds for local decrease.
 The function w is of unbounded variation on every interval.
 The quadratic variation of w over [0,t] is t.
 Zeros of the function w are a nowhere dense perfect set of Lebesgue measure 0 and Hausdorff dimension 1/2 (therefore, uncountable).
Quantitative properties
Law of the iterated logarithm
[math]\displaystyle{ \limsup_{t\to+\infty} \frac{ w(t) }{ \sqrt{ 2t \log\log t } } = 1, \quad \text{almost surely}. }[/math]
Modulus of continuity
Local modulus of continuity: [math]\displaystyle{ \limsup_{\varepsilon \to 0+} \frac{ w(\varepsilon) }{ \sqrt{ 2\varepsilon \log\log(1/\varepsilon) } } = 1, \qquad \text{almost surely}. }[/math]
Global modulus of continuity (Lévy): [math]\displaystyle{ \limsup_{\varepsilon\to0+} \sup_{0\le s\lt t\le 1, ts\le\varepsilon}\frac{w(s)w(t)}{\sqrt{ 2\varepsilon \log(1/\varepsilon)}} = 1, \qquad \text{almost surely}. }[/math]
Dimension doubling theorem
The dimension doubling theorems say that the Hausdorff dimension of a set under a Brownian motion doubles almost surely.
Local time
The image of the Lebesgue measure on [0, t] under the map w (the pushforward measure) has a density L_{t}. Thus, [math]\displaystyle{ \int_0^t f(w(s)) \, \mathrm{d}s = \int_{\infty}^{+\infty} f(x) L_t(x) \, \mathrm{d}x }[/math] for a wide class of functions f (namely: all continuous functions; all locally integrable functions; all nonnegative measurable functions). The density L_{t} is (more exactly, can and will be chosen to be) continuous. The number L_{t}(x) is called the local time at x of w on [0, t]. It is strictly positive for all x of the interval (a, b) where a and b are the least and the greatest value of w on [0, t], respectively. (For x outside this interval the local time evidently vanishes.) Treated as a function of two variables x and t, the local time is still continuous. Treated as a function of t (while x is fixed), the local time is a singular function corresponding to a nonatomic measure on the set of zeros of w.
These continuity properties are fairly nontrivial. Consider that the local time can also be defined (as the density of the pushforward measure) for a smooth function. Then, however, the density is discontinuous, unless the given function is monotone. In other words, there is a conflict between good behavior of a function and good behavior of its local time. In this sense, the continuity of the local time of the Wiener process is another manifestation of nonsmoothness of the trajectory.
Information rate
The information rate of the Wiener process with respect to the squared error distance, i.e. its quadratic ratedistortion function, is given by ^{[7]} [math]\displaystyle{ R(D) = \frac{2}{\pi^2 \ln 2 D} \approx 0.29D^{1}. }[/math] Therefore, it is impossible to encode [math]\displaystyle{ \{w_t \}_{t \in [0,T]} }[/math] using a binary code of less than [math]\displaystyle{ T R(D) }[/math] bits and recover it with expected mean squared error less than [math]\displaystyle{ D }[/math]. On the other hand, for any [math]\displaystyle{ \varepsilon\gt 0 }[/math], there exists [math]\displaystyle{ T }[/math] large enough and a binary code of no more than [math]\displaystyle{ 2^{TR(D)} }[/math] distinct elements such that the expected mean squared error in recovering [math]\displaystyle{ \{w_t \}_{t \in [0,T]} }[/math] from this code is at most [math]\displaystyle{ D  \varepsilon }[/math].
In many cases, it is impossible to encode the Wiener process without sampling it first. When the Wiener process is sampled at intervals [math]\displaystyle{ T_s }[/math] before applying a binary code to represent these samples, the optimal tradeoff between code rate [math]\displaystyle{ R(T_s,D) }[/math] and expected mean square error [math]\displaystyle{ D }[/math] (in estimating the continuoustime Wiener process) follows the parametric representation ^{[8]} [math]\displaystyle{ R(T_s,D_\theta) = \frac{T_s}{2} \int_0^1 \log_2^+\left[\frac{S(\varphi) \frac{1}{6}}{\theta}\right] d\varphi, }[/math] [math]\displaystyle{ D_\theta = \frac{T_s}{6} + T_s\int_0^1 \min\left\{S(\varphi)\frac{1}{6},\theta \right\} d\varphi, }[/math] where [math]\displaystyle{ S(\varphi) = (2 \sin(\pi \varphi /2))^{2} }[/math] and [math]\displaystyle{ \log^+[x] = \max\{0,\log(x)\} }[/math]. In particular, [math]\displaystyle{ T_s/6 }[/math] is the mean squared error associated only with the sampling operation (without encoding).
Related processes
The stochastic process defined by [math]\displaystyle{ X_t = \mu t + \sigma W_t }[/math] is called a Wiener process with drift μ and infinitesimal variance σ^{2}. These processes exhaust continuous Lévy processes.^{[clarification needed]}
Two random processes on the time interval [0, 1] appear, roughly speaking, when conditioning the Wiener process to vanish on both ends of [0,1]. With no further conditioning, the process takes both positive and negative values on [0, 1] and is called Brownian bridge. Conditioned also to stay positive on (0, 1), the process is called Brownian excursion.^{[9]} In both cases a rigorous treatment involves a limiting procedure, since the formula P(AB) = P(A ∩ B)/P(B) does not apply when P(B) = 0.
A geometric Brownian motion can be written [math]\displaystyle{ e^{\mu t\frac{\sigma^2 t}{2}+\sigma W_t}. }[/math]
It is a stochastic process which is used to model processes that can never take on negative values, such as the value of stocks.
The stochastic process [math]\displaystyle{ X_t = e^{t} W_{e^{2t}} }[/math] is distributed like the Ornstein–Uhlenbeck process with parameters [math]\displaystyle{ \theta = 1 }[/math], [math]\displaystyle{ \mu = 0 }[/math], and [math]\displaystyle{ \sigma^2 = 2 }[/math].
The time of hitting a single point x > 0 by the Wiener process is a random variable with the Lévy distribution. The family of these random variables (indexed by all positive numbers x) is a leftcontinuous modification of a Lévy process. The rightcontinuous modification of this process is given by times of first exit from closed intervals [0, x].
The local time L = (L^{x}_{t})_{x ∈ R, t ≥ 0} of a Brownian motion describes the time that the process spends at the point x. Formally [math]\displaystyle{ L^x(t) =\int_0^t \delta(xB_t)\,ds }[/math] where δ is the Dirac delta function. The behaviour of the local time is characterised by Ray–Knight theorems.
Brownian martingales
Let A be an event related to the Wiener process (more formally: a set, measurable with respect to the Wiener measure, in the space of functions), and X_{t} the conditional probability of A given the Wiener process on the time interval [0, t] (more formally: the Wiener measure of the set of trajectories whose concatenation with the given partial trajectory on [0, t] belongs to A). Then the process X_{t} is a continuous martingale. Its martingale property follows immediately from the definitions, but its continuity is a very special fact – a special case of a general theorem stating that all Brownian martingales are continuous. A Brownian martingale is, by definition, a martingale adapted to the Brownian filtration; and the Brownian filtration is, by definition, the filtration generated by the Wiener process.
Integrated Brownian motion
The timeintegral of the Wiener process [math]\displaystyle{ W^{(1)}(t) := \int_0^t W(s) \, ds }[/math] is called integrated Brownian motion or integrated Wiener process. It arises in many applications and can be shown to have the distribution N(0, t^{3}/3),^{[10]} calculated using the fact that the covariance of the Wiener process is [math]\displaystyle{ t \wedge s = \min(t, s) }[/math].^{[11]}
For the general case of the process defined by [math]\displaystyle{ V_f(t) = \int_0^t f'(s)W(s) \,ds=\int_0^t (f(t)f(s))\,dW_s }[/math] Then, for [math]\displaystyle{ a \gt 0 }[/math], [math]\displaystyle{ \operatorname{Var}(V_f(t))=\int_0^t (f(t)f(s))^2 \,ds }[/math] [math]\displaystyle{ \operatorname{cov}(V_f(t+a),V_f(t))=\int_0^t (f(t+a)f(s))(f(t)f(s)) \,ds }[/math] In fact, [math]\displaystyle{ V_f(t) }[/math] is always a zero mean normal random variable. This allows for simulation of [math]\displaystyle{ V_f(t+a) }[/math] given [math]\displaystyle{ V_f(t) }[/math] by taking [math]\displaystyle{ V_f(t+a)=A\cdot V_f(t) +B\cdot Z }[/math] where Z is a standard normal variable and [math]\displaystyle{ A=\frac{\operatorname{cov}(V_f(t+a),V_f(t))}{\operatorname{Var}(V_f(t))} }[/math] [math]\displaystyle{ B^2=\operatorname{Var}(V_f(t+a))A^2\operatorname{Var}(V_f(t)) }[/math] The case of [math]\displaystyle{ V_f(t)=W^{(1)}(t) }[/math] corresponds to [math]\displaystyle{ f(t)=t }[/math]. All these results can be seen as direct consequences of Itô isometry. The ntimesintegrated Wiener process is a zeromean normal variable with variance [math]\displaystyle{ \frac{t}{2n+1}\left ( \frac{t^n}{n!} \right )^2 }[/math]. This is given by the Cauchy formula for repeated integration.
Time change
Every continuous martingale (starting at the origin) is a time changed Wiener process.
Example: 2W_{t} = V(4t) where V is another Wiener process (different from W but distributed like W).
Example. [math]\displaystyle{ W_t^2  t = V_{A(t)} }[/math] where [math]\displaystyle{ A(t) = 4 \int_0^t W_s^2 \, \mathrm{d} s }[/math] and V is another Wiener process.
In general, if M is a continuous martingale then [math]\displaystyle{ M_t  M_0 = V_{A(t)} }[/math] where A(t) is the quadratic variation of M on [0, t], and V is a Wiener process.
Corollary. (See also Doob's martingale convergence theorems) Let M_{t} be a continuous martingale, and [math]\displaystyle{ M^_\infty = \liminf_{t\to\infty} M_t, }[/math] [math]\displaystyle{ M^+_\infty = \limsup_{t\to\infty} M_t. }[/math]
Then only the following two cases are possible: [math]\displaystyle{ \infty \lt M^_\infty = M^+_\infty \lt +\infty, }[/math] [math]\displaystyle{ \infty = M^_\infty \lt M^+_\infty = +\infty; }[/math] other cases (such as [math]\displaystyle{ M^_\infty = M^+_\infty = +\infty, }[/math] [math]\displaystyle{ M^_\infty \lt M^+_\infty \lt +\infty }[/math] etc.) are of probability 0.
Especially, a nonnegative continuous martingale has a finite limit (as t → ∞) almost surely.
All stated (in this subsection) for martingales holds also for local martingales.
Change of measure
A wide class of continuous semimartingales (especially, of diffusion processes) is related to the Wiener process via a combination of time change and change of measure.
Using this fact, the qualitative properties stated above for the Wiener process can be generalized to a wide class of continuous semimartingales.^{[12]}^{[13]}
Complexvalued Wiener process
The complexvalued Wiener process may be defined as a complexvalued random process of the form [math]\displaystyle{ Z_t = X_t + i Y_t }[/math] where [math]\displaystyle{ X_t }[/math] and [math]\displaystyle{ Y_t }[/math] are independent Wiener processes (realvalued).^{[14]}
Selfsimilarity
Brownian scaling, time reversal, time inversion: the same as in the realvalued case.
Rotation invariance: for every complex number [math]\displaystyle{ c }[/math] such that [math]\displaystyle{ c=1 }[/math] the process [math]\displaystyle{ c \cdot Z_t }[/math] is another complexvalued Wiener process.
Time change
If [math]\displaystyle{ f }[/math] is an entire function then the process [math]\displaystyle{ f(Z_t)  f(0) }[/math] is a timechanged complexvalued Wiener process.
Example: [math]\displaystyle{ Z_t^2 = \left(X_t^2  Y_t^2\right) + 2 X_t Y_t i = U_{A(t)} }[/math] where [math]\displaystyle{ A(t) = 4 \int_0^t Z_s^2 \, \mathrm{d} s }[/math] and [math]\displaystyle{ U }[/math] is another complexvalued Wiener process.
In contrast to the realvalued case, a complexvalued martingale is generally not a timechanged complexvalued Wiener process. For example, the martingale [math]\displaystyle{ 2 X_t + i Y_t }[/math] is not (here [math]\displaystyle{ X_t }[/math] and [math]\displaystyle{ Y_t }[/math] are independent Wiener processes, as before).
See also
Generalities:

Numerical path sampling:

Notes
 ↑ N.Wiener Collected Works vol.1
 ↑ Durrett, Rick (2019). "Brownian Motion". Probability: Theory and Examples (5th ed.). ISBN 9781108591034.
 ↑ Huang, Steel T.; Cambanis, Stamatis (1978). "Stochastic and Multiple Wiener Integrals for Gaussian Processes". The Annals of Probability 6 (4): 585–614. doi:10.1214/aop/1176995480. ISSN 00911798. https://www.jstor.org/stable/2243125.
 ↑ "Pólya's Random Walk Constants". https://mathworld.wolfram.com/PolyasRandomWalkConstants.html.
 ↑ Steven Lalley, Mathematical Finance 345 Lecture 5: Brownian Motion (2001)
 ↑ Shreve, Steven E (2008). Stochastic Calculus for Finance II: Continuous Time Models. Springer. pp. 114. ISBN 9780387401010.
 ↑ T. Berger, "Information rates of Wiener processes," in IEEE Transactions on Information Theory, vol. 16, no. 2, pp. 134139, March 1970. doi: 10.1109/TIT.1970.1054423
 ↑ Kipnis, A., Goldsmith, A.J. and Eldar, Y.C., 2019. The distortionrate function of sampled Wiener processes. IEEE Transactions on Information Theory, 65(1), pp.482499.
 ↑ Vervaat, W. (1979). "A relation between Brownian bridge and Brownian excursion". Annals of Probability 7 (1): 143–149. doi:10.1214/aop/1176995155.
 ↑ "Interview Questions VII: Integrated Brownian Motion – Quantopia" (in enUS). http://www.quantopia.net/interviewquestionsviiintegratedbrownianmotion/.
 ↑ Forum, "Variance of integrated Wiener process", 2009.
 ↑ Revuz, D., & Yor, M. (1999). Continuous martingales and Brownian motion (Vol. 293). Springer.
 ↑ Doob, J. L. (1953). Stochastic processes (Vol. 101). Wiley: New York.
 ↑
References
 Kleinert, Hagen (2004). Path Integrals in Quantum Mechanics, Statistics, Polymer Physics, and Financial Markets (4th ed.). Singapore: World Scientific. ISBN 9812381074. https://archive.org/details/pathintegralsinq0000klei. (also available online: PDFfiles)
 Stark, Henry; Woods, John (2002). Probability and Random Processes with Applications to Signal Processing (3rd ed.). New Jersey: Prentice Hall. ISBN 0130200719.
 Revuz, Daniel; Yor, Marc (1994). Continuous martingales and Brownian motion (Second ed.). SpringerVerlag.
External links
 Article for the schoolgoing child
 Brownian Motion, "Diverse and Undulating"
 Discusses history, botany and physics of Brown's original observations, with videos
 "Einstein's prediction finally witnessed one century later" : a test to observe the velocity of Brownian motion
 "Interactive Web Application: Stochastic Processes used in Quantitative Finance". http://turingfinance.com/interactivestochasticprocesses/.
Original source: https://en.wikipedia.org/wiki/Wiener process.
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