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In stochastic calculus, the Doléans-Dade exponential, Doléans exponential, or stochastic exponential, of a semimartingale X is defined to be the solution to the stochastic differential equation dYt = Yt dXt with initial condition Y0 = 1. The concept is named after Catherine Doléans-Dade. It is sometimes denoted by Ɛ(X). In the case where X is differentiable, then Y is given by the differential equation dY/dt = Y dX/dt to which the solution is Y = exp(XX0). Alternatively, if Xt = σBt + μt for a Brownian motion B, then the Doléans-Dade exponential is a geometric Brownian motion. For any continuous semimartingale X, applying Itō's lemma with ƒ (Y) = log(Y) gives


\begin{align}
d\log(Y) &= \frac{1}{Y}\,dY -\frac{1}{2Y^2}\,d[Y] \\
&= dX - \frac{1}{2}\,d[X].
\end{align}

Exponentiating gives the solution

Y_t = \exp\Bigl(X_t-X_0-\frac12[X]_t\Bigr),\qquad t\ge0.

This differs from what might be expected by comparison with the case where X is differentiable due to the existence of the quadratic variation term [X] in the solution.

The Doléans-Dade exponential is useful in the case when X is a local martingale. Then, Ɛ(X) will also be a local martingale whereas the normal exponential exp(X) is not. This is used in the Girsanov theorem. Criteria for a continuous local martingale X to ensure that its stochastic exponential Ɛ(X) is actually a martingale are given by Kazamaki's condition, Novikov's condition and Beneš' condition.

It is possible to apply Itō's lemma for non-continuous semimartingales in a similar way to show that the Doléans-Dade exponential of any semimartingale X is

Y_t = \exp\Bigl(X_t-X_0-\frac12[X]_t\Bigr)\prod_{s\le t}(1+\Delta X_s) \exp \Bigl(-\Delta X_s+\frac12\Delta X_s^2\Bigr),\qquad t\ge0,

where the product extents over the (countable many) jumps of X up to time t.

See also[edit]

References[edit]

  • Protter, Philip E. (2004), Stochastic Integration and Differential Equations (2nd ed.), Springer, ISBN 3-540-00313-4 

Original courtesy of Wikipedia: http://en.wikipedia.org/wiki/Doléans-Dade_exponential — Please support Wikipedia.
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