Wiener process


In mathematics, the Wiener process is a real valued continuous-time stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the one-dimensional Brownian motion. 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 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, instrument errors in filtering theory and unknown forces 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 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 ' is characterised by the following properties:
  1. has independent increments: for every the future increments are independent of the past values,
  2. has Gaussian increments: is normally distributed with mean and variance,
  3. has continuous paths: is continuous in.
That the process has independent increments means that if 0 ≤ s1 < t1 ≤ s2 < t2 then Wt1 − Ws1 and Wt2 − Ws2 are independent random variables, and the similar condition holds for n increments.
An alternative characterisation of the Wiener process is the so-called Lévy characterisation that says that the Wiener process is an almost surely continuous martingale with W0 = 0 and quadratic variation = t.
A third characterisation is that the Wiener process has a spectral representation as a sine series whose coefficients are independent N random variables. This representation can be obtained using the Karhunen–Loève theorem.
Another characterisation of a Wiener process is the definite integral of a zero mean, unit variance, delta correlated Gaussian process.
The Wiener process can be constructed as the scaling limit of a random walk, or other discrete-time 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 whereas it is not recurrent in dimensions three and higher. Unlike the random walk, it is scale invariant, meaning that
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, 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 be i.i.d. random variables with mean 0 and variance 1. For each n, define a continuous time stochastic process
This is a random step function. Increments of are independent because the are independent. For large n, is close to by the central limit theorem. Donsker's theorem asserts that as, approaches a Wiener process, which explains the ubiquity of Brownian motion.

Properties of a one-dimensional Wiener process

Basic properties

The unconditional probability density function, which follows normal distribution with mean = 0 and variance = t, at a fixed time t:
The expectation is zero:
The variance, using the computational formula, is t:
These results follow immediately from the definition that increments have a normal distribution, centered at zero. Thus

Covariance and correlation

The covariance and correlation :
These results follow from the definition that non-overlapping increments are independent, of which only the property that they are uncorrelated is used. Suppose that.
Substituting
we arrive at:
Since and are independent,
Thus
A corollary useful for simulation is that we can write, for t1 < t2:
where Z is an independent standard normal variable.

Wiener representation

Wiener also gave a representation of a Brownian path in terms of a random Fourier series. If are independent Gaussian variables with mean zero and variance one, then
and
represent a Brownian motion on. The scaled process
is a Brownian motion on .

Running maximum

The joint distribution of the running maximum
and Wt is
To get the unconditional distribution of, integrate over −∞ < w ≤ m :
the probability density function of a Half-normal distribution. The expectation is
If at time the Wiener process has a known value, it is possible to calculate the conditional probability distribution of the maximum in interval . The cumulative probability distribution function of the maximum value, conditioned by the known value, is:

Self-similarity

Brownian scaling

For every c > 0 the process is another Wiener process.

Time reversal

The process for 0 ≤ t ≤ 1 is distributed like Wt for 0 ≤ t ≤ 1.

Time inversion

The process is another Wiener process.

A class of Brownian martingales

If a polynomial p satisfies the PDE
then the stochastic process
is a martingale.
Example: is a martingale, which shows that the quadratic variation of W on is equal to t. It follows that the expected time of first exit of W from is equal to c2.
More generally, for every polynomial p the following stochastic process is a martingale:
where a is the polynomial
Example: the process
is a martingale, which shows that the quadratic variation of the martingale on is equal to
About functions p 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 of the Wiener process has all these properties almost surely.

Qualitative properties

[Law of the iterated logarithm]
[Modulus of continuity]
Local modulus of continuity:
Global modulus of continuity :

Local time

The image of the Lebesgue measure on under the map w has a density Lt. Thus,
for a wide class of functions f. The density Lt is continuous. The number Lt is called the local time at x of w on . It is strictly positive for all x of the interval where a and b are the least and the greatest value of w on , respectively. Treated as a function of two variables x and t, the local time is still continuous. Treated as a function of t, the local time is a singular function corresponding to a nonatomic measure on the set of zeros of w.
These continuity properties are fairly non-trivial. Consider that the local time can also be defined 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 non-smoothness of the trajectory.

Information rate

The information rate of the Wiener process with respect to the squared error distance, i.e. its quadratic rate-distortion function, is given by
Therefore, it is impossible to encode using a binary code of less than bits and recover it with expected mean squared error less than. On the other hand, for any, there exists large enough and a binary code of no more than distinct elements such that the expected mean squared error in recovering from this code is at most.
In many cases, it is impossible to encode the Wiener process without sampling it first. When the Wiener process is sampled at intervals before applying a binary code to represent these samples, the optimal trade-off between code rate and expected mean square error follows the parametric representation
where and. In particular, is the mean squared error associated only with the sampling operation.

Related processes

The stochastic process defined by
is called a Wiener process with drift μ and infinitesimal variance σ2. These processes exhaust continuous Lévy processes.
Two random processes on the time interval appear, roughly speaking, when conditioning the Wiener process to vanish on both ends of . With no further conditioning, the process takes both positive and negative values on and is called Brownian bridge. Conditioned also to stay positive on, the process is called Brownian excursion. In both cases a rigorous treatment involves a limiting procedure, since the formula P = P/P does not apply when P = 0.
A geometric Brownian motion can be written
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
is distributed like the Ornstein–Uhlenbeck process with parameters,, and.
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 is a left-continuous modification of a Lévy process. The right-continuous modification of this process is given by times of first exit from closed intervals .
The local time L = x ∈ R, t ≥ 0 of a Brownian motion describes the time that the process spends at the point x. Formally
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, and Xt the conditional probability of A given the Wiener process on the time interval . Then the process Xt 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 time-integral of the Wiener process
is called integrated Brownian motion or integrated Wiener process. It arises in many applications and can be shown to have the distribution N, calculated using the fact that the covariance of the Wiener process is.
For the general case of the process defined by
Then, for,
In fact, is always a zero mean normal random variable. This allows for simulation of given by taking
where Z is a standard normal variable and
The case of corresponds to. All these results can be seen as direct consequences of Itô isometry.
The n-times-integrated Wiener process is a zero-mean normal variable with variance. This is given by the Cauchy formula for repeated integration.

Time change

Every continuous martingale is a time changed Wiener process.
Example: 2Wt = V where V is another Wiener process.
Example. where and V is another Wiener process.
In general, if M is a continuous martingale then where A is the quadratic variation of M on , and V is a Wiener process.
Corollary. Let Mt be a continuous martingale, and
Then only the following two cases are possible:
other cases are of probability 0.
Especially, a nonnegative continuous martingale has a finite limit almost surely.
All stated for martingales holds also for local martingales.

Change of measure

A wide class of continuous semimartingales 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.

Complex-valued Wiener process

The complex-valued Wiener process may be defined as a complex-valued random process of the form where and are independent Wiener processes.

Self-similarity

Brownian scaling, time reversal, time inversion: the same as in the real-valued case.
Rotation invariance: for every complex number such that the process is another complex-valued Wiener process.

Time change

If is an entire function then the process is a time-changed complex-valued Wiener process.
Example: where
and is another complex-valued Wiener process.
In contrast to the real-valued case, a complex-valued martingale is generally not a time-changed complex-valued Wiener process. For example, the martingale is not.