Quasi-random numbers for copula models

Understand

The present work addresses the question how sampling algorithms for commonly applied copula models can be adapted to account for quasi-random numbers.

• Besides sampling methods such as the conditional distribution method (based on a one-to-one transformation), it is also shown that typically faster sampling methods (based on stochastic representations) can be used to improve upon classical Monte Carlo methods when pseudo-random number generators are replaced by quasi-random number generators.
• This opens the door to quasi-random numbers for models well beyond independent margins or the multivariate normal distribution.
• Detailed examples (in the context of finance and insurance), illustrations and simulations are given and software has been developed and provided in the R packages copula and qrng.