التفاصيل البيبلوغرافية
| العنوان: |
Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails. |
| المؤلفون: |
Contreras-Valdez, Mario Ivan1 (AUTHOR) marioivan.contrerasv@tec.mx, Sahu, Sonal2 (AUTHOR) sonalsahu@tec.mx, Núñez-Mora, José Antonio3 (AUTHOR) janm@tec.mx, Santillán-Salgado, Roberto Joaquín3 (AUTHOR) roberto.santillan@tec.mx |
| المصدر: |
Risks. Mar2024, Vol. 12 Issue 3, p50. 23p. |
| مصطلحات موضوعية: |
*FINANCIAL risk, *BUSINESS forecasting, *VALUE at risk, *STATISTICAL reliability, *CRYPTOCURRENCIES, INVERSE Gaussian distribution |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Risks is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
Full Text Finder