Forecasting market risk using ultra-high-frequency data and scaling laws

Jun, Qi; Yi, Lan; Chen, Yiyun

doi:10.1080/14697688.2018.1453166

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…In a broader economic context, Zhu et al (2017) present e pirical evidence of Bitcoin's susceptibility to economic indicators, while Qarni and Gulz (2021) highlight its role as a hedge against the US dollar and forex-related risks. Pranckeviči ūt ė (2011) underscores volatility modeling in risk assessment, advocating for high-frequency data, echoed by Qi et al (2018). This approach is further explored by (Huang and Lee 2013;Meng and Taylor 2020;Huang et al 2022), emphasizing highfrequency data's efficacy in refining VaR forecasts.…”

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

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“…The financial situation is the most important factor affecting the development of enterprises, so it is important to take these aspects into account when conducting research on them. It has also led to an increase in interest rate volatility, resulting in sharp changes in asset prices, which can cause problems such as lower yields and higher losses [4]. Therefore, it is important to study how to identify and measure risk, which requires in-depth analysis and investigation.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Application of Quantile Regression Equation in Forecasting Financial Value at Risk in Financial Markets

Chen

Hatamleh

2022

Applied Mathematics and Nonlinear Sciences

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With the development of the times and the progress of science and technology, the financial market is constantly reformed and China's financial industry is gradually modernized. However, China's economy has been in the stage of rough growth for a long time, which has led to low efficiency in the allocation of financial resources and unreasonable use of funds, and this has seriously restricted the whole social production activities and the stability and sustainable and healthy development of the national economy, so the prevention and control of financial risks is particularly important. Therefore, how to identify and measure the risks in China's financial industry and manage and invest them in a reasonable way is a very serious issue facing China's financial institutions today. On the other hand, due to the continuous development and innovation of the financial market, China's financial industry has been gradually reformed. However, due to the imperfection of China's economic system and related laws and regulations, some non-performing assets and irregularities in the operation of domestic financial institutions have emerged, and instability in the financial market has also emerged. These phenomena make China's financial industry face huge financial risks in the process of development.

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Big Data in Forecasting Research: A Literature Review

Tang

et al. 2022

Big Data Research

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Forecasting market risk using ultra-high-frequency data and scaling laws

Cited by 6 publications

References 22 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

A Study on the Application of Quantile Regression Equation in Forecasting Financial Value at Risk in Financial Markets

Big Data in Forecasting Research: A Literature Review

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