No abstract

No abstract

Monte Carlo methods are often applied to problems in finance especially in the area of risk calculation by the Value-atRisk (VaR) measure. Different applications of statistical resampling techniques are shown, specifically bootstrapping, to refine the computational results in different ways. Methods are provided for improving backtesting stability, acceleration of Monte Carlo VaR convergence by orders of magnitude, and incorporating covariance matrix uncertainty in VaR figures. Existing methods are applied and new solutions developed. Extensive numerical tests on large numbers of randomly generated portfolios prove the effectiveness of the suggested solutions.Value-AT-RISK, Monte Carlo, Resampling, Variance Reduction, Finance,

No abstract

No abstract