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In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over
Get started GPBoost is a software library for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. The GPBoost library is predominantly written in C++, and there exist both a
Risk measures: a generalization from the univariate to the matrix-variate This paper proposes a method to calculate matrix-variate value-at-risk. This paper develops a method for estimating the value-at-risk and the conditional value-at-risk when the underlying risk factors follow a beta distribution in a univariate and matrix-variate setting. Analytical expressions of the risk measures are developed. A numerical solution for the risk measures for any parameterization of beta distributed loss variables is presented. Of fundamental importance is the application of computer-based algorithms for...