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Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management

Author

Listed:
  • Michele Leonardo Bianchi

    (Banca d'Italia, Italy)

  • Stoyan V Stoyanov

    (Stony Brook University, USA)

  • Gian Luca Tassinari

    (University of Bologna, Italy)

  • Frank J Fabozzi

    (EDHEC Business School, France)

  • Sergio M Focardi

    (Léonard De Vinci University, France)

Abstract

The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their statistical properties is in itself a very useful endeavor from the point of view of managing assets and controlling risk. In this book, the authors are primarily concerned with the statistical properties of heavy-tailed distributions and with the processes that exhibit jumps. A detailed overview with a Matlab implementation of heavy-tailed models applied in asset management and risk managements is presented. The book is not intended as a theoretical treatise on probability or statistics, but as a tool to understand the main concepts regarding heavy-tailed random variables and processes as applied to real-world applications in finance. Accordingly, the authors review approaches and methodologies whose realization will be useful for developing new methods for forecasting of financial variables where extreme events are not treated as anomalies, but as intrinsic parts of the economic process.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Michele Leonardo Bianchi & Stoyan V Stoyanov & Gian Luca Tassinari & Frank J Fabozzi & Sergio M Focardi, 2019. "Handbook of Heavy-Tailed Distributions in Asset Management and Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 11118, December.
  • Handle: RePEc:wsi:wsbook:11118
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    Citations

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    Cited by:

    1. José Antonio Climent Hernández & Gabino Sánchez Arzate & Ambrosio Ortiz Ramírez, 2021. "Portafolios ?-estables del G20: Evidencia empírica con Markowitz, Tobin y CAPM," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(4), pages 1-28, Octubre -.
    2. Michele Leonardo Bianchi & Giovanni De Luca & Giorgia Rivieccio, 2020. "CoVaR with volatility clustering, heavy tails and non-linear dependence," Papers 2009.10764, arXiv.org.
    3. Michele Leonardo Bianchi, 2023. "Assessing and forecasting the market risk of bank securities holdings: a data-driven approach," Risk Management, Palgrave Macmillan, vol. 25(4), pages 1-23, December.
    4. Massimo Arnone & Michele Leonardo Bianchi & Anna Grazia Quaranta & Gian Luca Tassinari, 2021. "Catastrophic risks and the pricing of catastrophe equity put options," Computational Management Science, Springer, vol. 18(2), pages 213-237, June.
    5. Marco Cococcioni & Francesco Fiorini & Michele Pagano, 2023. "Modelling Heavy Tailed Phenomena Using a LogNormal Distribution Having a Numerically Verifiable Infinite Variance," Mathematics, MDPI, vol. 11(7), pages 1-16, April.
    6. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    7. Michele Leonardo Bianchi & Alberto Maria Sorrentino, 2020. "Measuring CoVaR: An Empirical Comparison," Computational Economics, Springer;Society for Computational Economics, vol. 55(2), pages 511-528, February.
    8. Zaevski, Tsvetelin S. & Nedeltchev, Dragomir C., 2023. "From BASEL III to BASEL IV and beyond: Expected shortfall and expectile risk measures," International Review of Financial Analysis, Elsevier, vol. 87(C).
    9. Michele Leonardo Bianchi & Asmerilda Hitaj & Gian Luca Tassinari, 2020. "Multivariate non-Gaussian models for financial applications," Papers 2005.06390, arXiv.org.
    10. Bianchi, Michele Leonardo & De Luca, Giovanni & Rivieccio, Giorgia, 2023. "Non-Gaussian models for CoVaR estimation," International Journal of Forecasting, Elsevier, vol. 39(1), pages 391-404.

    Book Chapters

    The following chapters of this book are listed in IDEAS

    More about this item

    Keywords

    Heavy Tail Distributions; Fat Tail Distributions; Lévy Processes; Tempered Stable Distributions; Multivariate Time-changed Brownian Motion; Extreme Value Theory; Risk Management;
    All these keywords.

    JEL classification:

    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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