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 …

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 …

the underlying statistical distributions, a variety of analyticalmethods and simulation-based methods are available. Aside … orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … and incremental VaR in either a non-normal analytical setting or a MonteCarlo / historical simulation context.This paper …

VaR calculation that will be developed in the form of High-order kernel estimator of VaR with historical simulation method … with Historical Simulation estimation methods and the combination of high order kernels increase with increasing order … kernel estimates and tend to be larger than the Historical Simulation estimation methods. Statistical properties indicates …

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 …