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An introduction to Multilevel Monte Carlo with applications to options.PrimeFaces.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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2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
##### Abstract [en]

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

2019. , p. 71
##### Keywords [en]

Multilevel Monte Carlo, Options, Mathematical finance, Simulation, Stochastic Differential Equations, Computational complexity, Strong convergence, Weak convergence, Euler-Maruyama, Milstein.
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:umu:diva-166671OAI: oai:DiVA.org:umu-166671DiVA, id: diva2:1381035
#####

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##### Supervisors

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##### Examiners

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt463",{id:"formSmash:j_idt463",widgetVar:"widget_formSmash_j_idt463",multiple:true}); Available from: 2020-01-14 Created: 2019-12-20 Last updated: 2020-01-14Bibliographically approved

^{A standard problem in mathematical ﬁnance is the calculation of the price of some ﬁnancial derivative such as various types of options. Since there exists analytical solutions in only a few cases it will often boil down to estimating the price with Monte Carlo simulation in conjunction with some numerical discretization scheme. The upside of using what we can call standard Monte Carlo is that it is relative straightforward to apply and can be used for a wide variety of problems. The downside is that it has a relatively slow convergence which means that the computational cost or complexity can be very large.}

However, this slow convergence can be improved upon by using Multilevel Monte Carlo instead of standard Monte Carlo. With this approach it is possible to reduce the computational complexity and cost of simulation considerably.

The aim of this thesis is to introduce the reader to the Multilevel Monte Carlo method with applications to European and Asian call options in both the Black-Scholes-Merton (BSM) model and in the Heston model. To this end we ﬁrst cover the necessary background material such as basic probability theory, estimators and some of their properties, the stochastic integral, stochastic processes and Ito’s theorem. We introduce stochastic diﬀerential equations and two numerical discretizations schemes, the Euler–Maruyama scheme and the Milstein scheme. We deﬁne strong and weak convergence and illustrate these concepts with examples. We also describe the standard Monte Carlo method and then the theory and implementation of Multilevel Monte Carlo. In the applications part we perform numerical experiments where we compare standard Monte Carlo to Multilevel Monte Carlo in conjunction with the Euler–Maruyama scheme and Milsteins scheme.

In the case of a European call in the BSM model, using the Euler–Maruyama scheme, we achieved a cost O(ε^{-2}(log ε)^{2}) to reach the desired error in accordance with theory in comparison to the O(ε^{-3}) cost for standard Monte Carlo. When using Milsteins scheme instead of the Euler–Maruyama scheme it was possible to reduce the cost in terms of the number of simulations needed to achieve the desired error even further. By using Milsteins scheme, a method with greater order of strong convergence than Euler–Maruyama, we achieved the O(ε^{-2}) cost predicted by the complexity theorem compared to the standard Monte Carlo cost of order O(ε^{-3}). In the ﬁnal numerical experiment we applied the Multilevel Monte Carlo method together with the Euler–Maruyama scheme to an Asian call in the Heston model. In this case, where the coeﬃcients of the Heston model do not satisfy a global Lipschitz condition, the study of strong or weak convergence is much harder. The numerical experiments suggested that the strong convergence was slightly slower compared to what was found in the case of a European call in the BSM model. Nevertheless, we still achieved substantial savings in computational cost compared to using standard Monte Carlo.

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