24 August, 2017
Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance
Stochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.
Juho Häppölä (born 1986, Riihimäki, Finland) finished his Finnish matriculation examination in 2005, and joined Helsinki University of Technology (currently, Aalto University). Having taken time off to complete his national service, he graduated witha Masters degree with distinction in engineering physics and mathematics. During his studies in Aalto University, he held numerous short-term research and teaching positions in Department of Mathematics and Systems Analysis, Olli V. Lounasmaa Laboratory, and Laboratory of Applied Physics. His final thesis project was carried out in P in Perimeter Institute for Theoretical Physics in Waterloo, Ontario, Canada. After a year in the energy commodity industry as a quantitative portfolio analyst, he joined KAUST in fall of 2012 as a PhD student. His Research at KAUST covers computational methods for stochastic dynamic systems, with computational finance as a field of application. In addition, he has volunteered on various open data initiatives, including contributing to the OpenStreetMap initiative in the Hejaz region. He is joining Bain & Company Nordics branch as a Senior Associate Consultant after his doctoral studies.Venue and time: Thursday, 24/08/2017 Time: 3:00 pm Location: Building 3 - Room 5209