A variance-covariance matrix decomposition technique that involves taking random sample from a multivariate normal distribution. In other words, it refers to the process of obtaining random samples from a multivariate normal distribution that takes into account the degrees of correlation among the variables involved. Cholesky decomposition is mainly used in simulation (e.g., Monte Carlo simulation) and multivariate option valuation. This type of decomposition is important in the analysis of risk factors and in the pricing of financial instruments and risk management (VaR modelling and copula models).