Cryptocurrency Derivatives: The Case of Bitcoin

Söylemez, Yakup

doi:10.1007/978-3-030-25275-5_25

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…To the best of our knowledge, despite a few trade articles explaining how crypto derivatives work (Lielacher, 2019; Shome, 2020; Söylemez, 2019), a widely accepted valuation model doesn't exist. The objective of the paper is to propose a sensible valuation model for bitcoin derivatives.…”

Section: Background Of the Bitcoin And Bitcoin Futuresmentioning

confidence: 99%

“…For example, Lielacher (2019) and Shome (2020) describe the basic terms of crypto futures and option contracts and provide some tips on how and where to trade crypto derivatives. Söylemez (2019) uses bitcoin derivatives as an example to illustrate the application of the blockchain technology from the perspective of the financial market innovation.…”

Section: Introductionmentioning

confidence: 99%

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Valuation of bitcoin options

Celik

2021

Journal of Futures Markets

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We propose an equilibrium valuation model for bitcoin options by extending Cao. Bitcoin is interpreted as a foreign currency in a small open economy where money supply and aggregate dividend are exogenous. The equilibrium bitcoin prices increase with diffusive and jump risks of these two exogenous factors. Analytical option pricing formulas are obtained with Merton's model as a special case. Static analysis reveals that a bitcoin call (put) option value increases (decreases) with the money supply growth rate. Numerical analysis shows that all risks lead to a positive premium in option prices relative to the Black–Scholes model.

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Section: Background Of the Bitcoin And Bitcoin Futuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Valuation of bitcoin options

Celik

2021

Journal of Futures Markets

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“…Exchanges and Providers (Silfversten et al, 2020) The Number of Support Coins (Moin et al, 2019) Degree of Difficulty in Operation (Rehman et al, 2019;Peters et al, 2016) Risks perspective Risk of Smart Contract Execution (Schär, 2020) Risk of Operational Security (Schär, 2020;Aziz, 2019) Risk of Legal and Regulatory (Söylemez, 2019;Rehman et al, 2019) Risk of Volatility in Token Price (Aziz, 2019;Rehman et al, 2019) Risk of Collapse (Rehman et al, 2019) 5. Token exchanges platforms: 5.1.…”

Section: Regulatedmentioning

confidence: 99%

Integrating the BWM and Topsis Algorithm to Evaluate the Optimal Token Exchanges Platform in Taiwan

Wang

Yang

Lin

2021

Technological and Economic Development of Economy

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This research presents procedures for determining the optimal solution of token exchanges platform for investors in Taiwan via integrating the best-worst method (BWM) and the technique for ordering preference by similarity to the ideal solution (TOPSIS). Firstly, this research applies the modified Delphi method to develop the perspectives and factors via literature review and experts opinion. Secondly, the BWM is implemented to obtain weights of perspectives and factors on the linear programming concept. Thirdly, the TOPSIS model is used to rank the optimal solution of the token exchange for investors or corporations. Finally, the proposed model BWMTOPSIS-based procedures will list the optimal token exchanges platform on the three token exchange platforms to investors or corporations in Taiwan on the basis of their rankings in the architecture. The proposed combination framework is able to provide academic and commerce support to investors or corporations in implementing the token into their portfolio as a valuable objective guide to determine the optimal token exchange platform.

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“…Studying the volatility dynamics of the cryptocurrency market is important for several scopes, among which managing risk effectively and hedging losses (Almeida and Gonçalves 2022), enhancing market efficiency through trading (Fang et al 2022) and designing derivatives (Söylemez 2019;Akyildirim et al 2020;Soska et al 2021). In particular, the presence of derivatives in the market modulates the volatility transmissions among centralized cryptocurrency exchanges (Alexander et al 2022;Badenhorst et al 2018;Beneki et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A comparison of cryptocurrency volatility-benchmarking new and mature asset classes

Brini,

Lenz

2024

Financ Innov

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The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.

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Cryptocurrency Derivatives: The Case of Bitcoin

Cited by 7 publications

References 22 publications

Valuation of bitcoin options

Valuation of bitcoin options

Integrating the BWM and Topsis Algorithm to Evaluate the Optimal Token Exchanges Platform in Taiwan

A comparison of cryptocurrency volatility-benchmarking new and mature asset classes

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