SSRN Author: Andreas TsanakasAndreas Tsanakas SSRN Content
https://privwww.ssrn.com/author=850157
https://privwww.ssrn.com/rss/en-usTue, 17 Nov 2020 01:15:11 GMTeditor@ssrn.com (Editor)Tue, 17 Nov 2020 01:15:11 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Cascade Sensitivity MeasuresIn risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus explicitly capturing the direct impact of the stressed input factor, as well as indirect effects via other inputs. Furthermore, alternative representations of the cascade sensitivity measure are derived, allowing us to address practical issues, such as incomplete specification of the model and high computational costs. The applicability of the methodology is illustrated through the analysis of a ...
https://privwww.ssrn.com/abstract=3270839
https://privwww.ssrn.com/1962066.htmlMon, 16 Nov 2020 10:04:12 GMTREVISION: Cascade Sensitivity MeasuresIn risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus explicitly capturing the direct impact of the stressed input factor, as well as indirect effects via other inputs. Furthermore, alternative representations of the cascade sensitivity measure are derived, allowing us to address practical issues, such as incomplete specification of the model and high computational costs. The applicability of the methodology is illustrated through the analysis of a ...
https://privwww.ssrn.com/abstract=3270839
https://privwww.ssrn.com/1960793.htmlWed, 11 Nov 2020 16:59:00 GMTREVISION: Cascade Sensitivity MeasuresIn risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus explicitly capturing the direct impact of the stressed input factor, as well as indirect effects via other inputs. Furthermore, alternative representations of the cascade sensitivity measure are derived, allowing us to address practical issues, such as incomplete specification of the model and high computational costs. The applicability of the methodology is illustrated through the analysis of a ...
https://privwww.ssrn.com/abstract=3270839
https://privwww.ssrn.com/1960280.htmlTue, 10 Nov 2020 15:38:53 GMTNew: Discrimination-Free Insurance PricingA simple formula for non-discriminatory insurance pricing is introduced. This formula is based on the assumption that certain individual (discriminatory) policyholder information is not allowed to be used for insurance pricing. The suggested procedure can be summarized as follows: First, we construct a price that is based on all available information, including discriminatory information. Thereafter, we average out the effect of discriminatory information. This averaging out is done such that discriminatory information can also not be inferred from the remaining non-discriminatory one, thus, neither allowing for direct nor for indirect discrimination.
https://privwww.ssrn.com/abstract=3520676
https://privwww.ssrn.com/1865227.htmlSun, 09 Feb 2020 23:40:08 GMTNew: Scenario Weights for Importance Measurement (SWIM) – An R Package for Sensitivity AnalysisThe SWIM package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-at-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional ...
https://privwww.ssrn.com/abstract=3515274
https://privwww.ssrn.com/1865211.htmlSun, 09 Feb 2020 22:53:40 GMT