Design Index-Based Hedging: Bundled Loss Property and Hybrid Genetic Algorithm

Wang, Frank Xuyan

doi:10.1007/978-3-319-20466-6_29

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2019

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“…The optimization function NMinimize and FindMinimum in Mathematica sometimes can only find a local optimum at best. As shown in [21,22], the DyHF and the CMODE algorithms are the two best no-adjustment-needed global optimization algorithms. Now that the C 2 oDE algorithm is better than these two [23], it would be desirable to see how it works on the GB2 fit problem.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

What Determines EP Curve Shape?

Wang¹

2019

Applied Mathematics

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Propose use kurtosis divided by skewness squared as shape factor, and use the global or conditional minimum/maximum of this shape factor for selecting and differentiating distribution families. Semi-empirical formulas for that lower/upper bound are calculated for various distribution families, with the aid of Computer Algebra System, for fitting hard to match distributions. Previous studies show high CV distribution is hard to fit and simulate, this study extends that conclusion to cases with low CV but still hard to match EP curves, characterized by having shape factors close to 1. The maximal likelihood approach of distribution fit can tell us which distribution family is better suited for an empirical distribution, but the shape factor range information can tell us why a distribution cannot fit well, or is not suitable at all. So the shape factor, in a sense, determines the EP curve shape.

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Section: Conclusion and Discussionmentioning

confidence: 99%

What Determines EP Curve Shape?

Wang¹

2019

Applied Mathematics

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show abstract

Design Index-Based Hedging: Bundled Loss Property and Hybrid Genetic Algorithm

Cited by 1 publication

References 21 publications

What Determines EP Curve Shape?

What Determines EP Curve Shape?

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