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Generalised geometric Brownian motion

  • Classical option pricing schemes assume that the value of a financial asset follows a geometric Brownian motion (GBM). However, a growing body of studies suggest that a simple GBM trajectory is not an adequate representation for asset dynamics, due to irregularities found when comparing its properties with empirical distributions. As a solution, we investigate a generalisation of GBM where the introduction of a memory kernel critically determines the behaviour of the stochastic process. We find the general expressions for the moments, log-moments, and the expectation of the periodic log returns, and then obtain the corresponding probability density functions using the subordination approach. Particularly, we consider subdiffusive GBM (sGBM), tempered sGBM, a mix of GBM and sGBM, and a mix of sGBMs. We utilise the resulting generalised GBM (gGBM) in order to examine the empirical performance of a selected group of kernels in the pricing of European call options. Our results indicate that the performance of a kernel ultimately dependsClassical option pricing schemes assume that the value of a financial asset follows a geometric Brownian motion (GBM). However, a growing body of studies suggest that a simple GBM trajectory is not an adequate representation for asset dynamics, due to irregularities found when comparing its properties with empirical distributions. As a solution, we investigate a generalisation of GBM where the introduction of a memory kernel critically determines the behaviour of the stochastic process. We find the general expressions for the moments, log-moments, and the expectation of the periodic log returns, and then obtain the corresponding probability density functions using the subordination approach. Particularly, we consider subdiffusive GBM (sGBM), tempered sGBM, a mix of GBM and sGBM, and a mix of sGBMs. We utilise the resulting generalised GBM (gGBM) in order to examine the empirical performance of a selected group of kernels in the pricing of European call options. Our results indicate that the performance of a kernel ultimately depends on the maturity of the option and its moneyness.show moreshow less

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Metadaten
Author details:Viktor StojkoskiORCiD, Trifce SandevORCiDGND, Lasko BasnarkovORCiD, Ljupco KocarevGND, Ralf MetzlerORCiDGND
DOI:https://doi.org/10.3390/e22121432
ISSN:1099-4300
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/33353060
Title of parent work (English):Entropy
Subtitle (English):theory and applications to option pricing
Publisher:MDPI
Place of publishing:Basel
Publication type:Article
Language:English
Date of first publication:2020/12/18
Publication year:2020
Release date:2023/07/06
Tag:Black– Scholes model; Fokker– Planck equation; geometric Brownian motion; option pricing
Volume:22
Issue:12
Article number:1432
Number of pages:34
Funding institution:German Science Foundation (DFG)German Research Foundation (DFG) [ME; 1535/6-1]; Alexander von Humboldt FoundationAlexander von Humboldt; Foundation; Alexander von Humboldt Polish Honorary Research Scholarship; from the Foundation for Polish Science (Fundacja na rzecz Nauki; Polskiej, FNP)
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
DDC classification:5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik
Peer review:Referiert
Publishing method:Open Access / Gold Open-Access
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License (German):License LogoCC-BY - Namensnennung 4.0 International
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