Outline

American options, derived from stocks, are important financial instruments for speculation and risk hedging. For example, an Apple option with a €150 predetermined price enables buying (selling) at that price if Apple stock rises above (falls below) €150. The financial market has a large book of options, with Apple alone having over 1,900 options. As stock prices fluctuate continuously, corresponding options and the associated metrics, must be re-estimated accordingly.
Accurately updating these values in real time has been a significant challenge for over five decades. Machine Learning (ML)presents a promising solution to real time American option pricing. The limited number of option parameters can be constructed as ML input-output pairs to ML prediction algorithms. The fast processing speed of ML enables more efficient handling of repetitive pricing tasks. This FinTech project aims to develop ML approaches that offer precise real-time option pricing, crucial for trading platforms and investment banks in informed market speculations and effective risk management.
Internship
This project includes a 3-month paid placement provided by Allied Irish Banks (AIB) Financial Risk Team during Year 2 of the student's doctoral studies. The PhD student will participate in relevant work projects, training programs and workshops in the Group Risk division.
The PhD student in this project will develop key research skills in both financial modelling and python data analytics, and gain industry experience on creating Fintech solutions for the real world. The student will be based at TU Dublin Aungier St Campus, Dublin 2, Ireland. The student will work under the supervision of Dr. Qianru (Jennifer) Shang, Asst. Prof. in Business Analytics, Dr. Brian Byrne, Asst. Prof. in Finance and Prof. Sarah Jane Delany, Prof. in Inclusive Computer Science.

Research Centre

School of Business Technology, Retail & Supply Chain | TU Dublin

https://www.tudublin.ie/explore/faculties-and-schools/business/business…

What is funded

Fully Funded (scholarship, fees, materials)

Funding agency This project is supported by TU Dublin and funded via the Technological University R&I supporting Enterprise scheme, which is co-financed by the Government of Ireland and the European Union through the ERDF Regional Programme 2021-2027

Student Stipend per annum €25,000

Materials & Travel Budget €2000 for materials with an additional €1500 provided in year 1 to cover the costs of a laptop. €1000 for travel to conferences etc

Fees €5500 covered

Duration

Duration of funding 48 months

Eligibility

The candidate should have minimum 2.1 BSc or MSc in a quantitative discipline (e.g. Quantitative Finance, Computer Science, Econometrics, Mathematics), and have experience in programming (e.g. Python, R, VBA, C/C++). Knowledge and experience of derivative pricing, machine learning and academic publication would be an advantage. Applicants whose first language is not English must show evidence of English proficiency. For details https://www.tudublin.ie/study/international-students/entry-requirements….

Application Deadline:12/08/24

If you are interested in submitting an application for this project, please complete an Expression of Interest.

https://forms.office.com/e/0hCcrv2Gkp