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Pagnottoni, P. (2020) Neural Network Models for Bitcoin Option Pricing. Frontiers in Artificial Intelligence, 2, 5. https://doi.org/10.3389/frai.2019.00005

has been cited by the following article:

• TITLE: Pricing Bitcoin under Double Exponential Jump-Diffusion Model with Asymmetric Jumps Stochastic Volatility

  AUTHORS: Ndeye Fatou Sene, Mamadou Abdoulaye Konte, Jane Aduda

  KEYWORDS: Bitcoin, Double Exponential Jump-Diffusion, Stochastic Volatility, Markov Chain Monte Carlo

  JOURNAL NAME: Journal of Mathematical Finance, Vol.11 No.2, May 31, 2021

  ABSTRACT: The objective of this study is, to show the importance of incorporating jumps in both returns and volatility dynamics for Bitcoin. For that purpose, we introduce the Double Exponential Jump-Diffusion model with Stochastic Volatility (DEJDSVJ) that contains asymmetric jumps. The use of the Markov Chain Monte Carlo methods for estimation has proved the meaningful presence of jumps in Bitcoin price and volatility. Moreover, based on the Bitcoin options market, a comparison between the underlying model, the Double Exponential Jump Diffusion model (DEJD) with Stochastic Volatility (no Jumps) and the Stochastic Volatility (SV) shows the goodness of the DEJDSVJ model’s calibration over others for pricing Bitcoin options.