Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
A statistical model for random rotations
Journal of Multivariate Analysis
Simulation of Brownian motion at first-passage times
Mathematics and Computers in Simulation
A double-threshold GARCH model of stock market and currency shocks on stock returns
Mathematics and Computers in Simulation
Testing for nonlinearity in mean and volatility for heteroskedastic models
Mathematics and Computers in Simulation
Modelling and managing financial risk: An overview
Mathematics and Computers in Simulation
Modelling the financial risk associated with U.S. movie box office earnings
Mathematics and Computers in Simulation
Polynomial chaos for simulating random volatilities
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation
Mathematics and Computers in Simulation
A unitary Hessenberg QR-based algorithm via semiseparable matrices
Journal of Computational and Applied Mathematics
A New Scaling and Squaring Algorithm for the Matrix Exponential
SIAM Journal on Matrix Analysis and Applications
Mathematics and Computers in Simulation
Original Articles: t-Copula generation for control variates
Mathematics and Computers in Simulation
Value-at-Risk for country risk ratings
Mathematics and Computers in Simulation
Monte Carlo option pricing with asymmetric realized volatility dynamics
Mathematics and Computers in Simulation
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Random orthogonal matrix (ROM) simulation is a very fast procedure for generating multivariate random samples that always have exactly the same mean, covariance and Mardia multivariate skewness and kurtosis. This paper investigates how the properties of parametric, data-specific and deterministic ROM simulations are influenced by the choice of orthogonal matrix. Specifically, we consider how cyclic and general permutation matrices alter their time-series properties, and how three classes of rotation matrices - upper Hessenberg, Cayley, and exponential - influence both the unconditional moments of the marginal distributions and the behaviour of skewness when samples are concatenated. We also perform an experiment which demonstrates that parametric ROM simulation can be hundreds of times faster than equivalent Monte Carlo simulation.