Thank you for your interest in applying to VSRP internships at KAUST.
We are happy to share that our in-person internship program has restarted!
However, due to the pandemic, all VSRP applicants applying for in-person internship, must obtain and show proof of full vaccination (vaccine certificates for COVID 19) with one of the following vaccines to be processed as VSRP interns: 2 doses of Pfizer BioNTech, 2 doses of Oxford AstraZeneca, 2 doses of Moderna,1 dose of Johnson and Johnson’s Janssen, 2 Doses of Sinopharm vaccine and one dose of Pfizer, Moderna, AstraZeneca, or Janssen, or 2 Doses of Sinovac vaccine and one dose of Pfizer, Moderna, AstraZeneca, or Janssen.
We are continuing to offer Remote/Virtual internship. If you are interested in a Remote VSRP internship, please check our “Internship page” and apply to the project of your choice. You may upload a blank page in lieu of vaccination proof in your application form, and mention that you are applying for a Remote internship in your Statement of Purpose.
Multilevel and Unbiased Monte Carlo Methods for Option Pricing
This project will focus on the numerical estimation of financial options. The latter are contracts associated to financial objects such as stocks, which can be mathematically expressed as expectations with respect to a diffusion process. In this project, we will develop Monte Carlo methods which can provide numerical estimates of these option prices, which are not available in closed forms. In particular, the diffusion processes will have to be time-discretized and we will use advanced multilevel and unbiased techniques to provide estimates sometimes with no time-discretization error.
Program -Applied Mathematics and Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Field of Study -Computational and Numerical Methods
About the Researcher
Professor, Applied Mathematics and Computational Science
Professor Jasra's research interests are in the development, analysis and application of Monte Carlo algorithms for problems in Bayesian Statistics, uncertainty quantification and computational finance. He has several interests in applied probability, including Markov chains and interacting particle systems. He has also worked on stochastic control problems and filtering (data assimilation).