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Numerical Complexity Analysis of Weak Approximation of Stochastic Differential EquationsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2002 (English)Doctoral thesis, comprehensive summary (Other scientific)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Stockholm: KTH , 2002. , x, 28 p.
##### Series

Trita-NA, ISSN 0348-2952 ; 0220
##### Keyword [en]

Adaptive methods, a posteriori error estimates, stochastic differential equations, weak approximation, Monte Carlo methods, Malliavin Calculus, HJM model, option price, bond market, stochastic elliptic equation, Karhunen-Loeve expansion, numerical co
##### National Category

Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-3413ISRN: KTH/NA/R--20/20--SEISBN: 91-7283-350-5 (print)OAI: oai:DiVA.org:kth-3413DiVA: diva2:9210
##### Public defence

2002-10-11, 00:00
#####

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##### Note

QC 20100825Available from: 2002-09-26 Created: 2002-09-26 Last updated: 2010-08-25Bibliographically approved
##### List of papers

The thesis consists of four papers on numerical complexityanalysis of weak approximation of ordinary and partialstochastic differential equations, including illustrativenumerical examples. Here by numerical complexity we mean thecomputational work needed by a numerical method to solve aproblem with a given accuracy. This notion offers a way tounderstand the efficiency of different numerical methods.

The first paper develops new expansions of the weakcomputational error for Ito stochastic differentialequations using Malliavin calculus. These expansions have acomputable leading order term in a posteriori form, and arebased on stochastic flows and discrete dual backward problems.Beside this, these expansions lead to efficient and accuratecomputation of error estimates and give the basis for adaptivealgorithms with either deterministic or stochastic time steps.The second paper proves convergence rates of adaptivealgorithms for Ito stochastic differential equations. Twoalgorithms based either on stochastic or deterministic timesteps are studied. The analysis of their numerical complexitycombines the error expansions from the first paper and anextension of the convergence results for adaptive algorithmsapproximating deterministic ordinary differential equations.Both adaptive algorithms are proven to stop with an optimalnumber of time steps up to a problem independent factor definedin the algorithm. The third paper extends the techniques to theframework of Ito stochastic differential equations ininfinite dimensional spaces, arising in the Heath Jarrow Mortonterm structure model for financial applications in bondmarkets. Error expansions are derived to identify differenterror contributions arising from time and maturitydiscretization, as well as the classical statistical error dueto finite sampling.

The last paper studies the approximation of linear ellipticstochastic partial differential equations, describing andanalyzing two numerical methods. The first method generates iidMonte Carlo approximations of the solution by sampling thecoefficients of the equation and using a standard Galerkinfinite elements variational formulation. The second method isbased on a finite dimensional Karhunen- Lo`eve approximation ofthe stochastic coefficients, turning the original stochasticproblem into a high dimensional deterministic parametricelliptic problem. Then, adeterministic Galerkin finite elementmethod, of either h or p version, approximates the stochasticpartial differential equation. The paper concludes by comparingthe numerical complexity of the Monte Carlo method with theparametric finite element method, suggesting intuitiveconditions for an optimal selection of these methods. 2000Mathematics Subject Classification. Primary 65C05, 60H10,60H35, 65C30, 65C20; Secondary 91B28, 91B70.

1. Adaptive weak approximation of stochastic differential equations$(function(){PrimeFaces.cw("OverlayPanel","overlay339597",{id:"formSmash:j_idt482:0:j_idt486",widgetVar:"overlay339597",target:"formSmash:j_idt482:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Convergence rates for adaptive weak approximation of stochastic differential equations$(function(){PrimeFaces.cw("OverlayPanel","overlay332839",{id:"formSmash:j_idt482:1:j_idt486",widgetVar:"overlay332839",target:"formSmash:j_idt482:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Monte Carlo euler approximation if HJM term structure financial models$(function(){PrimeFaces.cw("OverlayPanel","overlay345002",{id:"formSmash:j_idt482:2:j_idt486",widgetVar:"overlay345002",target:"formSmash:j_idt482:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Galerkin finite element approximations of stochastic elliptic partial differential equations$(function(){PrimeFaces.cw("OverlayPanel","overlay342289",{id:"formSmash:j_idt482:3:j_idt486",widgetVar:"overlay342289",target:"formSmash:j_idt482:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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