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Fully Flexible Views: Theory and Practice

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  • Attilio Meucci

Abstract

We propose a unified methodology to input non-linear views from any number of users in fully general non-normal markets, and perform, among others, stress-testing, scenario analysis, and ranking allocation. We walk the reader through the theory and we detail an extremely efficient algorithm to easily implement this methodology under fully general assumptions. As it turns out, no repricing is ever necessary, hence the methodology can be readily applied to books with complex derivatives. We also present an analytical solution, useful for benchmarking, which per se generalizes notable previous results. Code illustrating this methodology in practice is available at http://www.mathworks.com/matlabcentral/fileexchange/21307

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  • Attilio Meucci, 2010. "Fully Flexible Views: Theory and Practice," Papers 1012.2848, arXiv.org.
    • Handle: RePEc:arx:papers:1012.2848
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    Cited by:

    1. Pier Francesco Procacci & Tomaso Aste, 2022. "Portfolio optimization with sparse multivariate modeling," Journal of Asset Management, Palgrave Macmillan, vol. 23(6), pages 445-465, October.
    2. Miquel Noguer i Alonso & Sonam Srivastava, 2020. "Deep Reinforcement Learning for Asset Allocation in US Equities," Papers 2010.04404, arXiv.org.
    3. Adil Rengim Cetingoz & Olivier Gu'eant, 2023. "Asset and Factor Risk Budgeting: A Balanced Approach," Papers 2312.11132, arXiv.org, revised May 2024.
    4. Carlo Nicolini & Monisha Gopalan & Jacopo Staiano & Bruno Lepri, 2024. "Hopfield Networks for Asset Allocation," Papers 2407.17645, arXiv.org.

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