Quantitative Analysis.
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I.Basic math.
1.Conditional probability.
2.Normal distribution.
3.Brownian motion.
4.Poisson process.
5.Ito integral.
6.Ito calculus.
7.Change of measure.
8.Girsanov's theorem.
9.Forward Kolmogorov's equation.
10.Backward Kolmogorov's equation.
A.Multidimensional backward Kolmogorov's equation.
B.Representation of solution for elliptic PDE using stochastic process.
11.Optimal control, Bellman equation, Dynamic programming.
II.Pricing and Hedging.
III.Explicit techniques.
IV.Data Analysis.
V.Implementation tools.
VI.Basic Math II.
VII.Implementation tools II.
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Forum Notation Index Contents

Representation of solution for elliptic PDE using stochastic process.


roposition

Let $U$ be a bounded subset of $\QTR{cal}{R}^{n}$ with $C^{1}$-boundary. Let MATH and $f|_{\partial U}=0$, MATH. The solution of the boundary problemMATH is given byMATH where the $W_{t}$ is the standard Brownian motion and MATH is the first time when the process $x+W_{t}$ exits $U$.

Proof

We verify for one dimension:MATH By the compatibility condition $f|_{\partial U}=0$ we have MATH. We continueMATHMATH Note thatMATH henceMATH

Proof

We give another proof more inline with considerations of the section ( Backward_equation_section). LetMATH

where MATH. So that MATH. We calculate as in the section ( Backward_equation_section) and under assumption that $x$ is away from the boundary MATH:MATHMATH It remains to note that MATH Indeed,MATH for any $h$.





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