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Machine learning solutions to challenges in finance: An application to the pricing of financial products

Gan, lirong, Wang, Huamao, Yang, Zhaojun (2020) Machine learning solutions to challenges in finance: An application to the pricing of financial products. Technological Forecasting and Social Change, 153 . Article Number 119928. ISSN 0040-1625. (doi:10.1016/j.techfore.2020.119928) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:79670)

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https://doi.org/10.1016/j.techfore.2020.119928

Abstract

The recent fast development of machine learning provides new tools to solve challenges in many areas. In finance, average options are popular Financial products among corporations, institutional investors, and individual investors for risk management and investment because average options have the advantages of cheap prices and their payoffs are not very sensitive to the changes of the underlying asset prices at the maturity date, avoiding the manipulation of asset prices and option prices. The challenge is that pricing arithmetic average options requires traditional numerical methods with the drawbacks of expensive repetitive computations and non-realistic model assumptions. This paper proposes a machine-learning method to price arithmetic and geometric average options accurately and in particular quickly. The method is model-free and it is verired by empirical applications as well as numerical experiments.

Item Type: Article
DOI/Identification number: 10.1016/j.techfore.2020.119928
Uncontrolled keywords: Machine learning, Finance applications, Asian options, Model-free asset pricing, Financial technology
Subjects: T Technology
Divisions: Faculties > Social Sciences > Kent Business School
Depositing User: Huamao Wang
Date Deposited: 22 Jan 2020 15:42 UTC
Last Modified: 10 Sep 2020 08:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79670 (The current URI for this page, for reference purposes)
Wang, Huamao: https://orcid.org/0000-0003-0436-826X
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