Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data

Sheraz, Muhammad; Dedu, Silvia; Preda, Vasile

doi:10.3390/e24101410

Cited by 6 publications

(3 citation statements)

References 64 publications

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“…For example, the application of arithmetic coding methods in cryptographic information protection systems is considered in [42]. Volatile time series and information flow analyses based on cryptocurrency data have been studied in [43]. A new approach to predicting cryptocurrency is stated in [44].…”

Section: Discussionmentioning

confidence: 99%

On the Use of Variability Measures to Analyze Source Coding Data Based on the Shannon Entropy

et al. 2023

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Source coding maps elements from an information source to a sequence of alphabetic symbols. Then, the source symbols can be recovered exactly from the binary units. In this paper, we derive an approach that includes information variation in the source coding. The approach is more realistic than its standard version. We employ the Shannon entropy for coding the sequences of a source. Our approach is also helpful for short sequences when the central limit theorem does not apply. We rely on a quantifier of the information variation as a source. This quantifier corresponds to the second central moment of a random variable that measures the information content of a source symbol; that is, considering the standard deviation. An interpretation of typical sequences is also provided through this approach. We show how to use a binary memoryless source as an example. In addition, Monte Carlo simulation studies are conducted to evaluate the performance of our approach. We apply this approach to two real datasets related to purity and wheat prices in Brazil.

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Section: Discussionmentioning

confidence: 99%

On the Use of Variability Measures to Analyze Source Coding Data Based on the Shannon Entropy

et al. 2023

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“…al. [ 5 ], Moretto et al [ 6 ], Remuzgo et al [ 7 ], Sheraz et al [ 8 ] and Toma and Leoni-Aubin [ 9 ]. One of the most important information measures, the Tsallis entropy, has attracted considerable interest in statistical physics and many other fields as well.…”

Section: Introductionmentioning

confidence: 99%

Tsallis Entropy for Loss Models and Survival Models Involving Truncated and Censored Random Variables

Preda

Dedu

Iatan

et al. 2022

Entropy

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The aim of this paper consists in developing an entropy-based approach to risk assessment for actuarial models involving truncated and censored random variables by using the Tsallis entropy measure. The effect of some partial insurance models, such as inflation, truncation and censoring from above and truncation and censoring from below upon the entropy of losses is investigated in this framework. Analytic expressions for the per-payment and per-loss entropies are obtained, and the relationship between these entropies are studied. The Tsallis entropy of losses of the right-truncated loss random variable corresponding to the per-loss risk model with a deductible d and a policy limit u is computed for the exponential, Weibull, χ2 or Gamma distribution. In this context, the properties of the resulting entropies, such as the residual loss entropy and the past loss entropy, are studied as a result of using a deductible and a policy limit, respectively. Relationships between these entropy measures are derived, and the combined effect of a deductible and a policy limit is also analyzed. By investigating residual and past entropies for survival models, the entropies of losses corresponding to the proportional hazard and proportional reversed hazard models are derived. The Tsallis entropy approach for actuarial models involving truncated and censored random variables is new and more realistic, since it allows a greater degree of flexibility and improves the modeling accuracy.

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“…In this regard, a number of studies, such asRogers and Satchell (1991) andYang and Zhang (2000), have introduced alternative measures of volatility to achieve a better understanding of the nature of ranges and their significance in predicting volatility Gillaizeau et al (2019),Sheraz et al (2022),. and Chen and Yang (2023) specifically apply those volatility measures for their study of Bitcoin and cryptocurrencies.…”

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Price dynamics and volatility jumps in bitcoin options

Chen,

Yang

2024

Financ Innov

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In the FinTech era, we contribute to the literature by studying the pricing of Bitcoin options, which is timely and important given that both Nasdaq and the CME Group have started to launch a variety of Bitcoin derivatives. We find pricing errors in the presence of market smiles in Bitcoin options, especially for short-maturity ones. Long-maturity options display more of a “smirk” than a smile. Additionally, the ARJI-EGARCH model provides a better overall fit for the pricing of Bitcoin options than the other ARJI-GARCH type models. We also demonstrate that the ARJI-GARCH model can provide more precise pricing of Bitcoin and its options than the SVCJ model in term of the goodness-of-fit in forecasting. Allowing for jumps is crucial for modeling Bitcoin options as we find evidence of time-varying jumps. Our empirical results demonstrate that the realized jump variation can describe the volatility behavior and capture the jump risk dynamics in Bitcoin and its options.

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Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data

Cited by 6 publications

References 64 publications

On the Use of Variability Measures to Analyze Source Coding Data Based on the Shannon Entropy

On the Use of Variability Measures to Analyze Source Coding Data Based on the Shannon Entropy

Tsallis Entropy for Loss Models and Survival Models Involving Truncated and Censored Random Variables

Price dynamics and volatility jumps in bitcoin options

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