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Multilevel Monte-Carlo methods and lower-upper bounds in Initial Margin computations

Abstract : The Multilevel Monte-Carlo (MLMC) method developed by Giles [Gil08] has a natural application to the evaluation of nested expectation of the form E [g(E [f (X, Y)|X])], where f, g are functions and (X, Y) a couple of independent random variables. Apart from the pricing of American-type derivatives, such computations arise in a large variety of risk valuations (VaR or CVaR of a portfolio, CVA), and in the assessment of margin costs for centrally cleared portfolios. In this work, we focus on the computation of Initial Margin. We analyze the properties of corresponding MLMC estimators, for which we provide results of asymptotical optimality; at the technical level, we have to deal with limited regularity of the outer function g (which might fail to be everywhere differentiable). Parallel to this, we investigate upper and lower bounds for nested expectations as above, in the spirit of primal/dual algorithms for stochastic control problems.
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Submitted on : Tuesday, January 7, 2020 - 12:17:42 PM
Last modification on : Monday, February 21, 2022 - 8:08:02 AM


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F Bourgey, S de Marco, Emmanuel Gobet, Alexandre Zhou. Multilevel Monte-Carlo methods and lower-upper bounds in Initial Margin computations. Monte Carlo Methods and Applications, De Gruyter, 2020, 26 (2), ⟨10.1515/mcma-2020-2062⟩. ⟨hal-02430430⟩



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