Multilevel Monte Carlo Methods And Lower-Upper Bounds In Initial Margin Computations

The multilevel Monte Carlo (MLMC) method developed by M. B. Giles [Multilevel Monte Carlo path simulation, Oper. Res. 56 (2008), no. 3, 607-617] has a natural application to the evaluation of nested expectations E[g(IE[f(X, Y)vertical bar X]) 1, 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 asymptotic 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.