Machine learning for pricing European basket options

Ling, Biwen

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https://hdl.handle.net/2142/106406 Copy

Description

Title

Machine learning for pricing European basket options

Author(s)

Ling, Biwen

Issue Date

2019-12-13

Director of Research (if dissertation) or Advisor (if thesis)

• Linders, Daniel
• Chong, Alfred

Department of Study

Mathematics

Discipline

Actuarial Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

partial dependence structure, gaussian process regression

Abstract

In this thesis, we show how to deploy machine learning techniques such as Gaussian process regression to approximate the European basket option prices. For the underlying asset of European basket option, we assume it follows multivariate Black \&\ Scholes model, and we can derive the PDE for the option price. In order to deal with the curse of dimensionality, we assume that the basket consists of several comonotonic groups, in each comonotonic group, the stock prices are driven by a single random source. Then we can derive an approximation for the price of European basket option. Next, we introduce the finite difference scheme to price the European basket option for given parameters such as risk-free interest rates, maturities and so on. However, for approximating the European basket option for different risk-free interest rates, maturities and strikes, using finite difference scheme to get corresponding approximations costs much time. Hence, in order to save time for approximating the European basket option for different risk-free interest rates, maturities and strikes, we deploy Gaussian process regression to fit the training set produced by finite difference scheme and after comparing the results, we can conclude that the errors are often well within reasonable limits and hence very acceptable from a practical point of view and the Gaussian process regression truly save much time.

Graduation Semester

2019-12

Type of Resource

text

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http://hdl.handle.net/2142/106406

Copyright and License Information

Copyright 2019 Biwen Ling

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