On the error rate of conditional quasi-Monte Carlo for discontinuous functions
Provides a theoretical guarantee for conditional QMC's superior performance on discontinuous functions, benefiting practitioners in computational finance.
The paper proves that conditional quasi-Monte Carlo (QMC) achieves a convergence rate of O(n^{-1+ε}) for discontinuous integrands, offering orders of magnitude error reduction over plain QMC in options pricing and Greeks estimation.
This paper studies the rate of convergence for conditional quasi-Monte Carlo (QMC), which is a counterpart of conditional Monte Carlo. We focus on discontinuous integrands defined on the whole of $R^d$, which can be unbounded. Under suitable conditions, we show that conditional QMC not only has the smoothing effect (up to infinitely times differentiable), but also can bring orders of magnitude reduction in integration error compared to plain QMC. Particularly, for some typical problems in options pricing and Greeks estimation, conditional randomized QMC that uses $n$ samples yields a mean error of $O(n^{-1+ε})$ for arbitrarily small $ε>0$. As a by-product, we find that this rate also applies to randomized QMC integration with all terms of the ANOVA decomposition of the discontinuous integrand, except the one of highest order.