Mba, Jules ClementMwambi, Sutene MwambetaniaPindza, Edson2023-06-272023-06-272022-03-30Mba, J.C.; Mwambi, S.M.; Pindza, E. A Monte Carlo Approach to Bitcoin Price Prediction with Fractional Ornstein-Uhlenbeck Lévy Process. Forecasting 2022, 1, 409–419. https://DOI.org/10.3390/forecast4020023.2304-8158 (online)10.3390/forecast4020023http://hdl.handle.net/2263/91206This work is dedicated in memory of late Sutene Mwambi who contributed significantly to it. Sutene passed away at the final stage of conclusion of this article.DATA AVAILABILITY STATEMENT : The data used for this study can be obtained from the authors upon request or visit Coinmarketcap: https://coinmarketcap.com/, accessed on 25 February 2022.Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The timevarying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders.en© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Levy processMemory dependenceBitcoinForecastingArticle