Univariate and Multivariate GARCH Models Applied to Bitcoin Futures Option Pricing

Venter, Pierre; Maré, Eben

doi:10.3390/jrfm14060261

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Zolfaghari M et al predicted stock indexes by applying a hybrid model combining Adaptive Wavelet Transform (AWT), Long and Short Term Memory (LSTM) and ARIMAX-GARCH series models [3]. Venter P-J et al compared the model price with the market price to show the pricing performance, and studied the application of single-variable and multivariable GARCH model in the pricing of bitcoin futures and options [4]. Wang X-C evaluated the options with the maximum or minimum of the two prices under the GARCH model [5].…”

Section: Introductionmentioning

confidence: 99%

Research on Quantitative Analysis Model of Financial Risk Value

Chen¹

2022

Proceedings of the 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022)

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With the continuous improvement of theoretical and practical research on financial development, especially under the condition of further innovation of China's multi-level capital market system, there are more and more uncertain factors causing financial risk. It is very necessary to strengthen the research on the value of financial risk. This paper studies the quantitative model of financial value at risk from the perspective of factor pricing model and GARCH model. The research results can predict and prevent various risks in the process of financial development. The research results can also reduce the scope and degree of losses, and provide a more accurate reference basis for the construction and empirical analysis of the financial risk value quantitative model.

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Section: Introductionmentioning

confidence: 99%

Research on Quantitative Analysis Model of Financial Risk Value

Chen¹

2022

Proceedings of the 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022)

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Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options

Sapna,

Mohan

2023

Comput Econ

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Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty

Siu

2024

JRFM

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This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.

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Univariate and Multivariate GARCH Models Applied to Bitcoin Futures Option Pricing

Cited by 4 publications

References 30 publications

Research on Quantitative Analysis Model of Financial Risk Value

Research on Quantitative Analysis Model of Financial Risk Value

Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options

Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty

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