English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We develop a novel deep learning approach for pricing European basket optionswritten on assets that follow jump-diffusion dynamics. The option pricingproblem is formulated as a partial integro-differential equation, which isapproximated via a new implicit-explicit minimizing movement time-steppingapproach, involving approximation by deep, residual-type Artificial NeuralNetworks (ANNs) for each time step. The integral operator is discretized viatwo different approaches: a) a sparse-grid Gauss--Hermite approximationfollowing localised coordinate axes arising from singular value decompositions,and b) an ANN-based high-dimensional special-purpose quadrature rule.Crucially, the proposed ANN is constructed to ensure the asymptotic behavior ofthe solution for large values of the underlyings and also leads to consistentoutputs with respect to a priori known qualitative properties of the solution.The performance and robustness with respect to the dimension of the methods areassessed in a series of numerical experiments involving the Mertonjump-diffusion model.

A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models