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Up- and down-correlations in normal variance mixture models

Author

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  • Ansari, Jonathan
  • Shushi, Tomer
  • Vanduffel, Steven

Abstract

We study conditional correlations in normal variance mixture models. For several families, we determine explicit formulas for up-(down-)correlations defined as the correlation between the sum of risks and an individual component, conditionally on the sum being large (small).

Suggested Citation

  • Ansari, Jonathan & Shushi, Tomer & Vanduffel, Steven, 2024. "Up- and down-correlations in normal variance mixture models," Statistics & Probability Letters, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:stapro:v:205:y:2024:i:c:s0167715223001736
    DOI: 10.1016/j.spl.2023.109949
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    References listed on IDEAS

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    1. Bryant Chen & William W.Y. Hsu & Jan-Ming Ho & Ming-Yang Kao, 2014. "Linear-time accurate lattice algorithms for tail conditional expectation," Algorithmic Finance, IOS Press, vol. 3(1-2), pages 87-140.
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    Cited by:

    1. Matthew A. Ohemeng & Tomasz J. Kozubowski, 2025. "Discrete-Continuous Dual Families, Reciprocal Laws, Random Summation, and Mixtures of Gaussian Distributions," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(2), pages 741-789, August.

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