Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets

Silahli, Baykar; Dingec, Kemal Dincer; Cifter, Atilla; Aydın, Nezir

doi:10.1016/j.frl.2019.101425

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…The findings of the study are supported by You (2019), for confirmation of statistical properties of Bitcoin exchange rates. Silahli et al, (2021) confirm the findings of the study regarding the application of Weibull distribution as an outperformer to other models. Other studies that augment the findings of this research and confirm the status of Weibull Distribution as the best fit include Imtiaz et al, (2021), Kohli et al, (2021), andShanaev et al, (2021).…”

Section: Conclusion and Policy Implicationssupporting

confidence: 88%

Parametric Distribution’s Scrutiny over the Exchange Rate of Bitcoin

Khalid

Arif

Khan

2022

IJMRES

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Purpose: The research aims to analyze the log-returns of Bitcoin exchange rates against the US Dollar and Chinese Yuan by applying parametric distributions for understanding behavior and suggesting a best-fitted distribution. Design/Methodology/Approach: Methodology involves the volatility risk analysis using the GARCH model for analyzing the behavior of Bitcoin Exchange rates of USD and CNY. Findings: The results showed that the Weibull distribution gives the best fit to both of the currencies’ exchange rates Implications/Originality/Value: The exchange rates of Bitcoin analyzed in this study in midst of myriad other cryptocurrencies using parametric distributions thereby encouraging the application of nonparametric and semiparametric distributions in similar scenarios. The application of this study would enable not only individual investors but also institutional investors and venture capital firms to stay informed of alternating trends and movements through distributions for predicting future returns.

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Section: Conclusion and Policy Implicationssupporting

confidence: 88%

Parametric Distribution’s Scrutiny over the Exchange Rate of Bitcoin

Khalid

Arif

Khan

2022

IJMRES

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“…Acereda et al (2020) find that more complex model specifications do not outperform the simpler ones for bitcoin VaR, as long as heavy-tailed distributions are used instead of the standard normal. Silahli et al (2021) also find that simple benchmark models succeed in various VaR backtests for several crypto assets.…”

Section: Englementioning

confidence: 74%

“…For instance Chu et al (2017) and Köchling et al (2020) find that IGARCH provides the optimal in-sample fit for bitcoin and other cryptocurrencies; and Bouoiyour and Selmi (2016) and Baur et al (2018) both find that bitcoin's variance process is integrated. The forecasting performance of EWMA volatility models is assessed by Catania et al (2019), Bazán-Palomino (2020), Nekhili and Sultan (2020) and Silahli et al (2021). Silahli et al (2021) also examine an even simpler equally-weighted moving average (EQMA) model as a benchmark, while Guesmi et al (2019) and Segnon and Bekiros (2020) use fractionally integrated models such as the FIGARCH and FIAPARCH.…”

Section: Survey Of Models Employedmentioning

confidence: 99%

“…The forecasting performance of EWMA volatility models is assessed by Catania et al (2019), Bazán-Palomino (2020), Nekhili and Sultan (2020) and Silahli et al (2021). Silahli et al (2021) also examine an even simpler equally-weighted moving average (EQMA) model as a benchmark, while Guesmi et al (2019) and Segnon and Bekiros (2020) use fractionally integrated models such as the FIGARCH and FIAPARCH. Liu et al (2020) consider several univariate level.…”

Section: Survey Of Models Employedmentioning

confidence: 99%

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Assessing the Accuracy of Exponentially Weighted Moving Average Models for Value-at-Risk and Expected Shortfall of Crypto Portfolios

Alexander

Dakos

2022

SSRN Journal

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“…Today, it is commonly used to assess product reliability and perform different kinds of reliability analysis [2] and modeling [3]. The Weibull distribution can fit a wide range of data from many different fields, including biology, medicine, economics, environmental science, and other kinds of science [4][5][6]. However, one of the most important application areas is engineering, where it is commonly used for the fatigue life prediction of different types of products, components, or elements [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Influence of Disinfectants on Airport Conveyor Belts

et al. 2021

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The coronavirus disease has influenced almost all of our everyday activities. Traveling and transportation have been influenced significantly and there is no doubt that air transportation has been restricted and therefore reduced considerably. It is predicted that the change back to pre-pandemic conditions will take several years, and so it is a reasonable assumption that disinfectants will be used more frequently for a long time. The presented article initially deals with the possible impacts of the pandemic on aircraft infrastructure—namely, on the influence of disinfectants on the rubber materials used, for example, in conveyor belts. The proposed methodology is based on the Weibull analysis for conveyor belt lifetime prediction regarding the impact of disinfectants. The Weibull distribution is a continuous probability distribution that can be applied as a theoretical model for statistical data processing. It was named after Weibull, who suggested shape, scale, and location parameters that made the distribution meaningful and useful. Currently, this distribution is applied in many areas, such as biology, economics, and hydrology. In engineering applications, it can be used for reliability and survival analysis. It is used mainly in cases where failure time is dependent on the operating hours, cycles, or age of the component. In the reliability area, it can be used, for example, to predict the lifetime or failure time of a component. To show the consequences of material changes due to the use of disinfectants, this article also presents a CAE (Computer Aided Engineering) analysis that was used for the evaluation of other hyperelastic material characteristics. This research is based on the results of experimental measurements, during which the influence of the types of disinfectant commonly used for the elimination of the coronavirus disease on airport conveyor belt rubber segments was tested. From the performed analysis, it was found that the influence of disinfectants on the material characteristics, including material hardness, elasticity, and static and dynamic loading, could be significant. Therefore, the probability of mechanical damage to the rubber part of the conveyor belt becomes higher, and time intervals for the maintenance or repair of airport conveyor belts should be considered.

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Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets

Cited by 25 publications

References 22 publications

Parametric Distribution’s Scrutiny over the Exchange Rate of Bitcoin

Parametric Distribution’s Scrutiny over the Exchange Rate of Bitcoin

Assessing the Accuracy of Exponentially Weighted Moving Average Models for Value-at-Risk and Expected Shortfall of Crypto Portfolios

Influence of Disinfectants on Airport Conveyor Belts

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