Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their … parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function …, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference or approximate Bayesian …

We develop a simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate … simulation of risk factors for the risk management of financial institutions. We use the Kollo measure of multivariate skewness …

methodology is thus termed "ROM simulation''. We discuss certain classes of random orthogonal matrices and show how each class … produces samples with different characteristics. ROM simulation has applications to many problems that are resolved using … illustration, we apply ROM simulation to determine the value-at-risk of a stock portfolio …

simulation experiment we conclude that among the studied analytical approaches the one based on approximating the distribution of … the multiple period shocks by a skew-t was the best. It was almost as good as the simulation based alternative. We also …

simulation exercise is informative about the actual ranking of various estimators when applied to a particular problem. We …