Quantum Neural Computation for Option Price Modelling

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dc.creatorIvancevic, Vladimir G.
dc.date2009-03-04
dc.date2009-03-19
dc.date.accessioned2026-07-07T12:53:32Z
dc.date.available2026-07-07T12:53:32Z
dc.descriptionWe propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter. In this paper, we present a bidirectional quantum associative memory model for the Black--Scholes--like option price evolution, consisting of a pair of coupled NLS equations, one governing the stochastic volatility and the other governing the option price, both self-organizing in an adaptive `market heat potential', trained by continuous Hebbian learning. This stiff pair of NLS equations is numerically solved using the method of lines with adaptive step-size integrator. Keywords: Option price modelling, Quantum neural computation, nonlinear Schrödinger equations, leverage effect, bidirectional associative memory
dc.description15 pages, 6 figures, Latex
dc.identifierhttps://arxiv.org/abs/0903.0680
dc.identifierhttp://arxiv.org/abs/0903.0680
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/223650
dc.subjectComputational Finance
dc.subjectAdaptation and Self-Organizing Systems
dc.subjectPattern Formation and Solitons
dc.subjectPricing of Securities
dc.titleQuantum Neural Computation for Option Price Modelling
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