Simulation techniques are an integral part of the toolkit required for the risk management of large portfolios of complex financial instruments. Here we will focus on Quasi-Monte Carlo techniques, which may provide a more efficient alternative to standard Monte Carlo, while retaining most of its generality and ease of application. We will address both recent theoretical advances, such as the use of Brownian-Bridge sampling for dimensional reduction, as well as practical implementation issues, such as the choice of quasi-random number generator and sample-error estimation.

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