Stochastic calculus


































Part of a series of articles about
Calculus

  • Fundamental theorem



  • Limits of functions

  • Continuity





  • Mean value theorem

  • Rolle's theorem










Specialized



  • Fractional

  • Malliavin

  • Stochastic

  • Variations



  • v

  • t

  • e


Stochastic calculus is a branch of mathematics that operates on stochastic processes. It allows a consistent theory of integration to be defined for integrals of stochastic processes with respect to stochastic processes. It is used to model systems that behave randomly.


The best-known stochastic process to which stochastic calculus is applied is the Wiener process (named in honor of Norbert Wiener), which is used for modeling Brownian motion as described by Louis Bachelier in 1900 and by Albert Einstein in 1905 and other physical diffusion processes in space of particles subject to random forces. Since the 1970s, the Wiener process has been widely applied in financial mathematics and economics to model the evolution in time of stock prices and bond interest rates.


The main flavours of stochastic calculus are the Itô calculus and its variational relative the Malliavin calculus. For technical reasons the Itô integral is the most useful for general classes of processes, but the related Stratonovich integral is frequently useful in problem formulation (particularly in engineering disciplines). The Stratonovich integral can readily be expressed in terms of the Itô integral. The main benefit of the Stratonovich integral is that it obeys the usual chain rule and therefore does not require Itô's lemma. This enables problems to be expressed in a coordinate system invariant form, which is invaluable when developing stochastic calculus on manifolds other than Rn.
The dominated convergence theorem does not hold for the Stratonovich integral, consequently it is very difficult to prove results without re-expressing the integrals in Itô form.




Contents






  • 1 Itô integral


  • 2 Stratonovich integral


  • 3 Applications


  • 4 References





Itô integral


Main article: Itô calculus

The Itô integral is central to the study of stochastic calculus. The integral ∫HdX{displaystyle int H,dX}int H,dX is defined for a semimartingale X and locally bounded predictable process H.[citation needed]



Stratonovich integral


Main article: Stratonovich integral

The Stratonovich integral of a semimartingale X{displaystyle X}X against another semimartingale Y can be defined in terms of the Itô integral as


∫0tXs−∘dYs:=∫0tXs−dYs+12[X,Y]tc,{displaystyle int _{0}^{t}X_{s-}circ dY_{s}:=int _{0}^{t}X_{s-}dY_{s}+{frac {1}{2}}left[X,Yright]_{t}^{c},} int_0^t X_{s-} circ d Y_s : = int_0^t X_{s-} d Y_s + frac{1}{2} left [ X, Yright]_t^c,

where [X, Y]tc denotes the quadratic covariation of the continuous parts of X
and Y. The alternative notation


∫0tXs∂Ys{displaystyle int _{0}^{t}X_{s},partial Y_{s}} int_0^t X_s , partial Y_s

is also used to denote the Stratonovich integral.



Applications


An important application of stochastic calculus is in quantitative finance, in which asset prices are often assumed to follow stochastic differential equations. In the Black–Scholes model, prices are assumed to follow geometric Brownian motion.



References



  • Fima C Klebaner, 2012, Introduction to Stochastic Calculus with Application (3rd Edition). World Scientific Publishing, .mw-parser-output cite.citation{font-style:inherit}.mw-parser-output q{quotes:"""""""'""'"}.mw-parser-output code.cs1-code{color:inherit;background:inherit;border:inherit;padding:inherit}.mw-parser-output .cs1-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-limited a,.mw-parser-output .cs1-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Lock-gray-alt-2.svg/9px-Lock-gray-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Lock-red-alt-2.svg/9px-Lock-red-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration{color:#555}.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration span{border-bottom:1px dotted;cursor:help}.mw-parser-output .cs1-hidden-error{display:none;font-size:100%}.mw-parser-output .cs1-visible-error{font-size:100%}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-right{padding-right:0.2em}
    ISBN 9781848168312


  • Szabados, T. S.; Székely, B. Z. (2008). "Stochastic Integration Based on Simple, Symmetric Random Walks". Journal of Theoretical Probability. 22: 203. arXiv:0712.3908. doi:10.1007/s10959-007-0140-8.
    Preprint




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