Local time (mathematics) explained

In the mathematical theory of stochastic processes, local time is a stochastic process associated with semimartingale processes such as Brownian motion, that characterizes the amount of time a particle has spent at a given level. Local time appears in various stochastic integration formulas, such as Tanaka's formula, if the integrand is not sufficiently smooth. It is also studied in statistical mechanics in the context of random fields.

Formal definition

For a continuous real-valued semimartingale

, the local time of at the point is the stochastic process which is informally defined by

where

is the Dirac delta function and is the quadratic variation. It is a notion invented by Paul Lévy. The basic idea is that is an (appropriately rescaled and time-parametrized) measure of how much time has spent at up to time . More rigorously, it may be written as the almost sure limit } \, d[B]_s,

which may be shown to always exist. Note that in the special case of Brownian motion (or more generally a real-valued diffusion of the form

where is a Brownian motion), the term simply reduces to , which explains why it is called the local time of at . For a discrete state-space process , the local time can be expressed more simply as^[1] }(X_s) \, ds.

Tanaka's formula

Tanaka's formula also provides a definition of local time for an arbitrary continuous semimartingale

on ^[2] A more general form was proven independently by Meyer^[3] and Wang;^[4] the formula extends Itô's lemma for twice differentiable functions to a more general class of functions. If is absolutely continuous with derivative which is of bounded variation, then where is the left derivative.

If

is a Brownian motion, then for any the field of local times has a modification which is a.s. Hölder continuous in with exponent , uniformly for bounded and .^[5] In general, has a modification that is a.s. continuous in and càdlàg in .

Tanaka's formula provides the explicit Doob–Meyer decomposition for the one-dimensional reflecting Brownian motion,

.

Ray–Knight theorems

The field of local times

associated to a stochastic process on a space is a well studied topic in the area of random fields. Ray–Knight type theorems relate the field L_t to an associated Gaussian process.

In general Ray–Knight type theorems of the first kind consider the field L_t at a hitting time of the underlying process, whilst theorems of the second kind are in terms of a stopping time at which the field of local times first exceeds a given value.

First Ray–Knight theorem

Let (B_t)_{t ≥ 0} be a one-dimensional Brownian motion started from B₀ = a > 0, and (W_t)_t≥0 be a standard two-dimensional Brownian motion started from W₀ = 0 ∈ R². Define the stopping time at which B first hits the origin,

. Ray^[6] and Knight^[7] (independently) showed thatwhere (L_t)_{t ≥ 0} is the field of local times of (B_t)_{t ≥ 0}, and equality is in distribution on C[0, ''a'']. The process |W_x|² is known as the squared Bessel process.

Second Ray–Knight theorem

Let (B_t)_{t ≥ 0} be a standard one-dimensional Brownian motion B₀ = 0 ∈ R, and let (L_t)_{t ≥ 0} be the associated field of local times. Let T_a be the first time at which the local time at zero exceeds a > 0

Let (W_t)_{t ≥ 0} be an independent one-dimensional Brownian motion started from W₀ = 0, then^[8] Equivalently, the process (which is a process in the spatial variable ) is equal in distribution to the square of a 0-dimensional Bessel process started at , and as such is Markovian.

Generalized Ray–Knight theorems

Results of Ray–Knight type for more general stochastic processes have been intensively studied, and analogue statements of both and are known for strongly symmetric Markov processes.

See also

• Tanaka's formula
• Brownian motion
• Random field

References

• K. L. Chung and R. J. Williams, Introduction to Stochastic Integration, 2nd edition, 1990, Birkhäuser, .
• M. Marcus and J. Rosen, Markov Processes, Gaussian Processes, and Local Times, 1st edition, 2006, Cambridge University Press
• P. Mörters and Y. Peres, Brownian Motion, 1st edition, 2010, Cambridge University Press, .

Notes and References

1. Book: Ioannis . Karatzas . Steven . Shreve . 1991 . Brownian Motion and Stochastic Calculus . Springer .
2. Book: Kallenberg . Foundations of Modern Probability . limited . New York . Springer . 1997 . 428–449 . 0387949577 .
3. Book: Meyer, Paul-Andre . Un cours sur les intégrales stochastiques . Séminaire de probabilités 1967–1980 . . 1771 . 174–329 . 2002 . 1976 . 10.1007/978-3-540-45530-1_11 . 978-3-540-42813-8 .
4. Wang . Generalized Itô's formula and additive functionals of Brownian motion . Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete . 41 . 2. 153–159 . 1977 . 10.1007/bf00538419. 123101077 . free .
5. Book: Kallenberg . Foundations of Modern Probability . limited . New York . Springer . 1997 . 370 . 0387949577 .
6. D. . Ray . Sojourn times of a diffusion process . . 7 . 4 . 615–630 . 1963 . 10.1215/ijm/1255645099. 0156383 . 0118.13403 . free .
7. F. B. . Knight . Random walks and a sojourn density process of Brownian motion . . 109 . 1 . 56–86 . 1963 . 1993647 . 10.2307/1993647. free .
8. Book: Marcus . Rosen . Markov Processes, Gaussian Processes and Local Times . limited . New York . Cambridge University Press . 2006 . 53–56 . 0521863007 .