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Don’t fall from the saddle: The importance of higher moments of credit loss distributions


  • ANNAERT, Jan
  • BATISTA, Crispiniano Garcia Joao
  • LAMOOT, Jeroen
  • LANINE, Gleb


The original Panjer recursion of the CreditRisk+ model is said to be unstable and therefore to yield inaccurate results of the tail distribution of credit portfolios. A much-hailed solution for the flaws of the Panjer recursion is the saddlepoint approximation method. In this paper we show that the saddlepoint approximation is an accurate and robust tool only for credit portfolios with low skewness and kurtosis of the loss distribution. However, often credit portfolios are heterogeneous with large skewness and kurtosis. We show that for such portfolios the commonly applied saddlepoint approximations (the Lugannani-Rice and the Barndorff-Nielsen formulas) are not reliable. We explain it by the dependence of the high-order standardized cumulants and the relative error on the saddlepoint. The more the cumulants and the relative error fluctuate for variations in the value of the saddlepoint (from 0 to the upper bound) the less accurate the saddlepoint approximation is. Hence, the saddlepoint approximations are not a universal substitute to the Panjer recursion algorithm. We also provide users of CR+ with a set of diagnostics to identify beforehand when the saddlepoint approximations are prone to failure.

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  • ANNAERT, Jan & BATISTA, Crispiniano Garcia Joao & LAMOOT, Jeroen & LANINE, Gleb, 2007. "Don’t fall from the saddle: The importance of higher moments of credit loss distributions," Working Papers 2007010, University of Antwerp, Faculty of Applied Economics.
  • Handle: RePEc:ant:wpaper:2007010

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    1. H. Evangelaras & C. Koukouvinos & A. M. Dean & C. A. Dingus, 2005. "Projection properties of certain three level orthogonal arrays," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 62(2), pages 241-257, November.
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    CreditRisk+; Saddlepoint approximations; Lugannani-Rice formula; Barndorff-Nielsen formula; Credit VaR;

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