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February, 1981 Stochastic Integration and $L^p$-Theory of Semimartingales
Klaus Bichteler
Ann. Probab. 9(1): 49-89 (February, 1981). DOI: 10.1214/aop/1176994509


If $X$ is a bounded left-continuous and piecewise constant process and if $Z$ is an arbitrary process, both adapted, then the stochastic integral $\int X dZ$ is defined as usual so as to conform with the sure case. In order to obtain a reasonable theory one needs to put a restriction on the integrator $Z$. A very modest one suffices; to wit, that $\int X_n dZ$ converge to zero in measure when the $X_n$ converge uniformly or decrease pointwise to zero. Daniell's method then furnishes a stochastic integration theory that yields the usual results, including Ito's formula, local time, martingale inequalities, and solutions to stochastic differential equations. Although a reasonable stochastic integrator $Z$ turns out to be a semimartingale, many of the arguments need no splitting and so save labor. The methods used yield algorithms for the pathwise computation of a large class of stochastic integrals and of solutions to stochastic differential equations.


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Klaus Bichteler. "Stochastic Integration and $L^p$-Theory of Semimartingales." Ann. Probab. 9 (1) 49 - 89, February, 1981.


Published: February, 1981
First available in Project Euclid: 19 April 2007

zbMATH: 0458.60057
MathSciNet: MR606798
Digital Object Identifier: 10.1214/aop/1176994509

Primary: 60H05

Rights: Copyright © 1981 Institute of Mathematical Statistics


Vol.9 • No. 1 • February, 1981
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