Abstract
An American call (put) option is a contract that gives the holder the right, but not the obligation, to buy (sell) one unit of an asset (typically, stock) at a prespecified price (called strike price) at any desired time before a preset expiration time of the contract. The associated option pricing problem plays an important role in modern financial markets and one way to solve this is by searching for the optimal exercise policy, i.e., find the optimal time to exercise so that maximal reward is achieved. In this thesis, we shall discuss the modern Least Square Policy Iteration Method to solve the American option pricing problem based on Reinforcement Learning and compare it to the method of the Longstaff-Schwartz Method and the Finite Difference Method.
Committee Chair
Professor José E. Figueroa-López.
Committee Members
Professor Mladen Victor Wickerhauser and Professor Jimin Ding.
Degree
Master of Arts (AM/MA)
Author's Department
Statistics
Document Type
Thesis
Date of Award
Spring 5-15-2020
Language
English (en)
DOI
https://doi.org/10.7936/hm07-f855
Recommended Citation
Hu, Chenshan, "American Option Pricing: From PDE Numerical Solutions to Simulation-Based Methods and Reinforcement Learning." (2020). Arts & Sciences Theses and Dissertations. 2035.
The definitive version is available at https://doi.org/10.7936/hm07-f855