Approximations of Profit-and-Loss Distributions (Part II)

working report - Former investigation (Approximation of Profit-and-Loss Distributions, Part I) introduces the application of the barycentric approximation methodology for evaluating profit-and-loss distributions numerically. Although, convergence of the quantiles is ensured by the weak convergence of the discrete measures, as proclaimed in Part I, recent numerical results have indicated that the approximations of the profit-and-loss distribution are less practical when the portfolio gets a reasonable complexity. This experience has revealed that the weak convergence of the probability measures appears not to be strong enough for evaluating quantiles numerically in a satisfactory way. Thereupon, the authors have focused on information offered by the barycentric approximation but still unused in the algorithmic procedure of Part I. It has been realized that the dual to the derived discrete probability measure helps evaluate the profit-and-loss distribution in a better way. In this Part II, the barycentric approximation technique is outlined and benchmarked with the intention to focus on the dual viewpoint for simplicial refinement. This technique poses no assumption on the risk factor space, except that the variance-covariance matrix of the risk factors exist. Therefore, it is applicable for general multivariate or empirical distributions. Furthermore, the technique provides approximation of the risk profile as well as of the risk factor distribution.Beforehand, various test environments are specified which help illustrate the sensitivity of value-at-risk numbers. These environments are characterized by the probability measure P of the risk factors and a risk profile g which represents the payoff structure of some portfolio. The corresponding numerical results illustrate the sensitivity of value-at-risk with respect to market volatility and correlation of risk factors. This provides information on the model risk one is exposed to within the value-at-risk approach.