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Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails

Author

Listed:
  • Mario Ivan Contreras-Valdez

    (Campus Ciudad de México, Tecnológico de Monterrey, Mexico City 14380, Mexico)

  • Sonal Sahu

    (Campus Guadalajara, Tecnológico de Monterrey, Colonia Nuevo México, Zapopan 45201, Mexico)

  • José Antonio Núñez-Mora

    (EGADE Business School, Tecnológico de Monterrey, Mexico City 01389, Mexico)

  • Roberto Joaquín Santillán-Salgado

    (EGADE Business School, Tecnológico de Monterrey, Mexico City 01389, Mexico)

Abstract

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

Suggested Citation

  • Mario Ivan Contreras-Valdez & Sonal Sahu & José Antonio Núñez-Mora & Roberto Joaquín Santillán-Salgado, 2024. "Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails," Risks, MDPI, vol. 12(3), pages 1-23, March.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:3:p:50-:d:1356275
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    References listed on IDEAS

    as
    1. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    2. Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, vol. 1(1), pages 1-14, June.
    3. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
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