Neural Network Models for Bitcoin Option Pricing.

Paolo Pagnottoni

doi:10.3389/frai.2019.00005

Abstract

Despite the current growing interest in Bitcoins—and cryptocurrencies in general—financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models—namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method—are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.

Highlights

Stock options are a category of financial derivatives which became widely employed by investors and speculators during the last few decades
Classical parametric option pricing methods lead to price predictions which are consistently lower than the actual option prices, both in the put and the call cases
It may be argued that options written on Bitcoin are systematically overpriced by the platform when considering the parametric methods in question

Summary

Introduction

Stock options are a category of financial derivatives which became widely employed by investors and speculators during the last few decades. Investors may ineffectively manage their portfolios if they are not able to value options in a proper way For this reason, a reliable methodology capable to yield an option’s current price or forecast is fundamental for investors in order to produce a rigorous decision making. A reliable methodology capable to yield an option’s current price or forecast is fundamental for investors in order to produce a rigorous decision making This is true when considering non-mature and volatile markets like the cryptocurrency one. The most widely known option pricing method is arguably the one defined by Black and Scholes (1973) This technique has been widely employed by practitioners, its strict set of assumptions, as well as subjectivity with respect to the parameter choices, often yields to unreliable results to some extent. The leptokurtic behavior of return distributions and the volatility smiles and skews are features that cannot be captured by such a simplistic technique

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