Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/37077
Title: Application of quantum master equation for long-term prognosis of asset-prices
Authors: Khrennikova, Polina
First Published: 12-Jan-2016
Publisher: Elsevier for North-Holland
Citation: Physica A: Statistical Mechanics and its Applications, 450, 2016, pp. 253–263
Abstract: This study combines the disciplines of behavioral finance and an extension of econophysics, namely the concepts and mathematical structure of quantum physics. We apply the formalism of quantum theory to model the dynamics of some correlated financial assets, where the proposed model can be potentially applied for developing a long-term prognosis of asset price formation. At the informational level, the asset price states interact with each other by the means of a “financial bath”. The latter is composed of agents’ expectations about the future developments of asset prices on the finance market, as well as financially important information from mass-media, society, and politicians. One of the essential behavioral factors leading to the quantum-like dynamics of asset prices is the irrationality of agents’ expectations operating on the finance market. These expectations lead to a deeper type of uncertainty concerning the future price dynamics of the assets, than given by a classical probability theory, e.g., in the framework of the classical financial mathematics, which is based on the theory of stochastic processes. The quantum dimension of the uncertainty in price dynamics is expressed in the form of the price-states superposition and entanglement between the prices of the different financial assets. In our model, the resolution of this deep quantum uncertainty is mathematically captured with the aid of the quantum master equation (its quantum Markov approximation). We illustrate our model of preparation of a future asset price prognosis by a numerical simulation, involving two correlated assets. Their returns interact more intensively, than understood by a classical statistical correlation. The model predictions can be extended to more complex models to obtain price configuration for multiple assets and portfolios.
DOI Link: 10.1016/j.physa.2015.12.135
ISSN: 0378-4371
Links: http://www.sciencedirect.com/science/article/pii/S0378437116000091
http://hdl.handle.net/2381/37077
Embargo on file until: 12-Jan-2017
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016, Elsevier. Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website. Following the embargo period the version associated with this record is distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Published Articles, School of Management

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