Model Risk and Differential Geometry Applied to Sensitivity Analysis

2018 ◽  
Author(s):  
Zuzana Krajcovicova ◽  
Pedro Pablo PPrez-Velasco
Author(s):  
Martin Hinsch ◽  
Jakub Bijak ◽  
Jason Hilton

AbstractThis chapter is devoted to the presentation of a more realistic version of the model, Risk and Rumours, which extends the previous, theoretical version (Routes and Rumours) by including additional empirical and experimental information following the process described in Part II of this book. We begin by offering a reflection on the integration of the five elements of the modelling process, followed by a more detailed description of the Risk and Rumours model, and how it differs from the previous version. Subsequently, we present selected results of the uncertainty and sensitivity analysis, enabling us to make further inference on the information gaps and areas for potential data collection. We also present model calibration for an empirically grounded version of the model, Risk and Rumours with Reality. In that way, we can evaluate to what extent the iterative modelling process has enabled a reduction in the uncertainty of the migrant route formation. In the final part of the chapter, we reflect on the model-building process and its implementation.


2016 ◽  
Vol 45 (1) ◽  
pp. 25-44 ◽  
Author(s):  
Kevin Jakob ◽  
Matthias Fischer

In this article we introduce the novel GCPM package, which represents a generalized credit portfolio model framework. The package includes two of the most popular mod- eling approaches in the banking industry namely the CreditRisk+ and the CreditMetrics model and allows to perform several sensitivity analysis with respect to distributional or functional assumptions. Therefore, besides the pure quanti?cation of credit portfolio risk, the package can be used to explore certain aspects of model risk individually for every arbitrary credit portfolio. In order to guarantee maximum ?exibility, most of the models utilize a Monte Carlo simulation, which is implemented in C++, to achieve the loss dis- tribution. Furthermore, the package also o?ers the possibilities to apply simple pooling techniques to speed up calculations for large portfolios as well as a general importance sample approach. The article concludes with a comprehensive example demonstrating the ?exibility of the package.


Author(s):  
M. Crampin ◽  
F. A. E. Pirani

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