New approaches to option pricing with expansion methodsThe dissertation of Jun Hu explores the possibility and structure of an infinite series solution to European options with stochastic volatility models and American options with the Black-Scholes model.
Closed-form solutions are extremely rare in option pricing, as they are only available for elementary models like the Black-Scholes model. Therefore, many numerical methods, e.g. the Monte Carlo, tree and finite difference approaches are used to price options with advanced models. However, they all have weakness, both in terms of domain of application and computational complexity.
The dissertation of MSc Jun Hu provides analytical perspectives to option pricing with expansion methods. The results show that with an appropriate choice of expansion form, a series solution is able to approximate the option price to a higher degree of accuracy than numerical methods. Moreover, expansion methods can provide more information on the prices, which are written in certain types of well-defined special functions with the same structure. Lastly, numerical demonstration shows that expansion methods are efficient to implement in practical calculations.
Public defense of a doctoral dissertation on Friday, 21 October
The doctoral dissertation of MSc Jun Hu in the field of financial mathematics entitled ‘Option Pricing with Expansion Methods: New Approaches to Advanced Stochastic Volatility Models and American Options’ will be publicly examined at the Department of Industrial Management of Tampere University of Technology (TUT) in Auditorium Pieni Sali 1 in the Festia building (address: Korkeakoulunkatu 8, Tampere, Finland) at 12 noon on Friday, 21 October 2016.
The opponent will be Professor Daniel Sevcovic (Comenius University). Professor Juho Kanniainen from the Department of Industrial Management at TUT will act as Chairman.
Jun Hu comes from China and he worked as a Marie Curie researcher in the HPCFinance project (www.hpcfinance.eu) at the Department of Industrial Management at TUT.
Jun Hu, tel. +358 (0) 50 301 0096, email@example.com