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Global Sensitivity Analysis for ABS

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Quantitative Assessment of Securitisation Deals

Part of the book series: SpringerBriefs in Finance ((BRIEFSFINANCE))

Abstract

In the previous chapter, we looked at the ratings variability due to model choice and parameter values. In the present chapter, we are going to investigate the ratings parameter sensitivity further by applying global sensitivity analysis techniques. Sensitivity analysis is a powerful methodology for analyzing how the uncertainty in the output can be allocated to the different uncertain input parameters. A sensitivity analysis is said to be global when the space of all possible combinations for the input parameters is explored at best, this distinguishes it from a local analysis, which is developed around a predetermined point in the space of the inputs. See, for example, [1] for an introduction to global sensitivity analysis.

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Notes

  1. 1.

    We have been using the ‘sobolset’ class (with the ‘MatousekAffineOwen’ scramble algorithm) and ‘RandStream’ class (with the ‘mrg32k3a’ generator algorithm) in MATLAB for generating Sobol sequences and pseudo random numbers, respectively.

  2. 2.

    As explained in [15], this is the preferred scheme when using the quasi random sequences mentioned in 6.2.2, as the points of matrix \(\mathbf A \) (and hence of \(\mathbf A^i_b \)) are somehow better than the points of \(\mathbf B \) (and hence of \(\mathbf B^i_a \)). For more details see [15].

  3. 3.

    This is not surprising because losses are allocated to the notes in reverse order of seniority, it is the junior tranche that absorbs any losses first.

  4. 4.

    We apply the method using \(r=10\) trajectories of \(4\) points. Having \(k=7\) input parameters, the total number of SA model evaluations is 80 (\(N = r(k+1)\)). This choice has been demonstrated to produce valuable results in a general application of the sensitivity analysis.

  5. 5.

    This choice has been demonstrated to produce valuable results in a general application of the variance based method (see [18]).

  6. 6.

    The cliff effect refers to the risk that a small change in one or several of the input assumptions generates a dramatic change in the rating.

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Author information

Authors and Affiliations

  1. Joint Research Centre, Scientific Support to Financial Analysis Unit, European Commission, Via Enrico Fermi 2749, Ispra, 21027, Italy

    Francesca Campolongo & Henrik Jönsson

  2. Department of Mathematics, Katholieke Universiteit Leuven, Celestijnenlaan 200 B, Leuven, 3001, Belgium

    Wim Schoutens

Authors
  1. Francesca Campolongo
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  2. Henrik Jönsson
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  3. Wim Schoutens
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Corresponding author

Correspondence to Francesca Campolongo .

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Campolongo, F., Jönsson, H., Schoutens, W. (2013). Global Sensitivity Analysis for ABS. In: Quantitative Assessment of Securitisation Deals. SpringerBriefs in Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29721-2_6

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