Stratonovich integral

In stochastic processes, the Stratonovich integral (developed simultaneously by Ruslan Stratonovich and Donald Fisk) is a stochastic integral, the most common alternative to the Itô integral. Although the Itô integral is the usual choice in applied mathematics, the Stratonovich integral is frequently used in physics.

In some circumstances, integrals in the Stratonovich definition are easier to manipulate. Unlike the Itô calculus, Stratonovich integrals are defined such that the chain rule of ordinary calculus holds.

Perhaps the most common situation in which these are encountered is as the solution to Stratonovich stochastic differential equations (SDEs). These are equivalent to Itô SDEs and it is possible to convert between the two whenever one definition is more convenient.


The Stratonovich integral can be defined in a manner similar to the Riemann integral, that is as a limit of Riemann sums. Suppose that is a Wiener process and is a semimartingale adapted to the natural filtration of the Wiener process. Then the Stratonovich integral

is a random variable defined as the limit in mean square of[1]

as the mesh of the partition of tends to 0 (in the style of a Riemann–Stieltjes integral).


Many integration techniques of ordinary calculus can be used for the Stratonovich integral, e.g.: if f:RR is a smooth function, then

and more generally, if f:R×RR is a smooth function, then

This latter rule is akin to the chain rule of ordinary calculus.

Numerical methods

Stochastic integrals can rarely be solved in analytic form, making stochastic numerical integration an important topic in all uses of stochastic integrals. Various numerical approximations converge to the Stratonovich integral, and variations of these are used to solve Stratonovich SDEs (Kloeden & Platen 1992). Note however that the most widely used Euler scheme (the Euler–Maruyama method) for the numeric solution of Langevin equations requires the equation to be in Itô form.[2]

Differential notation

If Xt, Yt and Zt are stochastic processes such that

for all T>0, we also write

This notation is often used to formulate stochastic differential equations (SDEs), which are really equations about stochastic integrals. It is compatible with the notation from ordinary calculus, for instance

Comparison with the Itô integral

The Itô integral of the process X with respect to the Wiener process W is denoted by

(without the circle). For its definition, the same procedure is used as above in the definition of the Stratonovich integral, except for choosing the value of the process at the left-hand endpoint of each subinterval, i.e.

in place of

This integral does not obey the ordinary chain rule as the Stratonovich integral does; instead one has to use the slightly more complicated Itô's lemma.

Conversion between Itô and Stratonovich integrals may be performed using the formula

where ƒ is any continuously differentiable function of two variables W and t and the last integral is an Itô integral (Kloeden & Platen 1992, p. 101).

It follows that if Xt is a time-homogeneous Itô diffusion with continuously differentiable diffusion coefficient σ (i.e. it satisfies the SDE ), we have

More generally, for any two semimartingales X and Y

where is the continuous part of the covariation.

Stratonovich integrals in applications

The Stratonovich integral lacks the important property of the Itô integral, which does not "look into the future". In many real-world applications, such as modelling stock prices, one only has information about past events, and hence the Itô interpretation is more natural. In financial mathematics the Itô interpretation is usually used.

In physics, however, stochastic integrals occur as the solutions of Langevin equations. A Langevin equation is a coarse-grained version of a more microscopic model; depending on the problem in consideration, Stratonovich or Itô interpretation or even more exotic interpretations such as the isothermal interpretation, are appropriate. The Stratonovich interpretation is the most frequently used interpretation within the physical sciences.

The Wong–Zakai theorem states that physical systems with non-white noise spectrum characterized by a finite noise correlation time τ can be approximated by a Langevin equations with white noise in Stratonovich interpretation in the limit where τ tends to zero.

Because the Stratonovich calculus satisfies the ordinary chain rule, stochastic differential equations (SDEs) in the Stratonovich sense are more straightforward to define on differentiable manifolds, rather than just on Rn. The tricky chain rule of the Itô calculus makes it a more awkward choice for manifolds.

Stratonovich interpretation and supersymmetric theory of SDEs

In supersymmetric theory of SDEs, the finite-time stochastic evolution operator is given its most natural mathematical meaning of the stochastically averaged pullback induced on the exterior algebra of the phase space by the noise-configuration-dependent SDE-defined diffeomorphisms. This operator is unique and corresponds to the Stratonovich interpretation of SDEs. In addition, Stratonovich approach is equivalent to the Weyl symmetrization convention needed for disambiguation of the stochastic evolution operator during the transition from path integral to its operator representation. Moreover, in the appendix of Ref.,[3] it is shown that the widespread argumentation stating that, unlike Ito approach, Stratonovich approach "looks" into the future is a misconception. None of the approaches to SDEs "look" into the future. The only advantage of the Ito approach is that the coordinate change at each time step is given as an explicit function of the current coordinate, whereas all other approaches to SDEs this function is implicit. This advantage, however, has no mathematical or physical significance and, consequently, the Ito approach does not have any advantages over, say, the Stratonovich approach to SDEs. At the same time, the use of the Ito approach leads to a stochastic evolution operator with the shifted flow vector field as compared to that of the original SDE under consideration.


  1. Gardiner (2004), p. 98 and the comment on p. 101
  2. Perez-Carrasco R.; Sancho J.M. (2010). "Stochastic algorithms for discontinuous multiplicative white noise" (PDF). Phys. Rev. E. 81 (3): 032104. Bibcode:2010PhRvE..81c2104P. doi:10.1103/PhysRevE.81.032104. PMID 20365796.
  3. Ovchinnikov, I.V. (2016). "Introduction to supersymmetric theory of stochastics". Entropy. 18 (4): 108. arXiv:1511.03393. Bibcode:2016Entrp..18..108O. doi:10.3390/e18040108.


  • Øksendal, Bernt K. (2003). Stochastic Differential Equations: An Introduction with Applications. Springer, Berlin. ISBN 3-540-04758-1.
  • Gardiner, Crispin W. (2004). Handbook of Stochastic Methods (3 ed.). Springer, Berlin Heidelberg. ISBN 3-540-20882-8.
  • Jarrow, Robert; Protter, Philip (2004). "A short history of stochastic integration and mathematical finance: The early years, 1880–1970". IMS Lecture Notes Monograph. 45: 1–17. CiteSeerX
  • Kloeden, Peter E.; Platen, Eckhard (1992). Numerical solution of stochastic differential equations. Applications of Mathematics. Berlin, New York: Springer-Verlag. ISBN 978-3-540-54062-5..
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.