Deep Kusuoka Approximation: High-Order Spatial Approximation for Solving High-Dimensional Kolmogorov Equations and Its Application to Finance

The paper introduces a new deep learning-based high-order spatial approximation for a solution of a high-dimensional Kolmogorov equation where the initial condition is only assumed to be a continuous function and the condition on the vector fields associated with the differential operator is very ge...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computational economics Ročník 64; číslo 3; s. 1443 - 1461
Hlavní autoři:	Naito, Riu, Yamada, Toshihiro
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2024 Springer Springer Nature B.V
Témata:	Approximation Behavioral/Experimental Economics Computation Computational mathematics Computer Appl. in Social and Behavioral Sciences Deep learning Differential equations Differentiation Economic Theory/Quantitative Economics/Mathematical Methods Economics Economics and Finance Fields (mathematics) Learning Math Applications in Computer Science Mathematical analysis Neural networks Numerical differentiation Operations Research/Decision Theory Operators (mathematics) Optimization techniques Partial differential equations Random variables Viscosity Deep learning Financial diffusions Kolmogorov equations Kusuoka approximation Delta computing
ISSN:	0927-7099, 1572-9974
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!