Increments of Normal Inverse Gaussian Process as Logarithmic Returns of Stock Price

Rubenis, Oskars; Matvejevs, Andrejs

doi:10.7250/itms-2018-0015

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The NIG distribution, effective in modeling stock returns and estimating option prices (Rubenis and Matvejevs 2018;Hamza et al 2015), belongs to the GH family of Lévy processes (Eberlein and Keller 1995). It outperforms multivariate models in VaR estimation (Mata et al 2021) and finds application in various financial contexts, including cryptocurrencies (Sayit 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Contreras-Valdez,

Sahu,

Núñez-Mora

et al. 2024

Risks

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Contreras-Valdez,

Sahu,

Núñez-Mora

et al. 2024

Risks

View full text Add to dashboard Cite

show abstract

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

Contreras-Valdez,

Sahu,

Núñez-Mora

et al. 2024

Preprint

View full text Add to dashboard Cite

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 from January 1st, 2017, to October 25th, 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 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.

show abstract

Increments of Normal Inverse Gaussian Process as Logarithmic Returns of Stock Price

Cited by 2 publications

References 3 publications

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

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

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

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