Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/25847
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Pirvu, Traian | - |
dc.contributor.author | Kevin Shuai Zhang | - |
dc.date.accessioned | 2020-10-02T18:21:44Z | - |
dc.date.available | 2020-10-02T18:21:44Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://hdl.handle.net/11375/25847 | - |
dc.description.abstract | In this manuscript, we develop a nite liquidity framework for two-asset markets. In contrast to the standard multi-asset Black-Scholes framework, trading in our market model has a direct impact on the asset's price. The price impact is incorporated into the dynamics of the first asset through a specific trading strategy, as in large trader liquidity models. We adopt Euler- Maruyama and Milstein scheme in the simulation of asset prices. Exchange and Spread option values are numerically estimated by Monte Carlo with the Margrabe option as a controlled variate. The time complexity of these numerical schemes is included. Finally, we provide some deep learning frameworks to implement these pricing models effectively. | en_US |
dc.language.iso | en | en_US |
dc.subject | Mathematical Finance | en_US |
dc.subject | Derivative Pricing | en_US |
dc.subject | Stochastic Analysis | en_US |
dc.subject | Computational Finance | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Deep Learning | en_US |
dc.title | Numerical Analysis of Two-Asset Options in a Finite Liquidity Framework | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Thesis.pdf | 3.47 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.