Modeling the price of Bitcoin with geometric fractional Brownian motion: a Monte Carlo approach
Mariusz Tarnopolski
TL;DR
This paper uses geometric fractional Brownian motion and Monte Carlo simulations to predict Bitcoin prices, leveraging its long-term dependence characterized by a Hurst exponent greater than 0.5.
Contribution
It introduces a Monte Carlo approach based on fractional Brownian motion to model Bitcoin prices, accounting for long-term dependence.
Findings
Most probable Bitcoin price at start of 2018: 6358 USD
Monte Carlo simulation with 10,000 realizations
Prediction accuracy of 10%
Abstract
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent , is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferences is 10\%. The most probable Bitcoin price at the beginning of 2018 is 6358 USD.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Market Dynamics and Volatility