Derivatives Pricing via Machine Learning

In this paper, we combine the theory of stochastic process and techniques of machine learning with the regression analysis, first proposed by Longstaff and Schwartz 2001 and apply the new methodologies on financial derivatives pricing. Rigorous convergence proofs are provided for some of the methods we propose. Numerical examples show good applicability of the algorithms

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Year of publication:	2019
Authors:	Ye, Tingting
Other Persons:	Zhang, Liangliang (contributor)
Publisher:	[2019]: [S.l.] : SSRN
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Derivat \| Derivative \| Optionspreistheorie \| Option pricing theory \| Kapitalmarkttheorie \| Financial economics \| Methodologie \| Methodology \| Vergleich \| Comparison \| Risikoprämie \| Risk premium \| Kapitalmarktrendite \| Capital market returns

Extent:	1 Online-Ressource (40 p)
Series:	Boston University Questrom School of Business Research Paper ; No. 3352688
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 30, 2019 erstellt
Other identifiers:	10.2139/ssrn.3352688 [DOI]
Classification:	C63 - Computational Techniques
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012890648