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Examination of new models and estimation methods for valuating financial derivatives

The dissertation of MSc Hanxue Yang explores the efficiency and robustness of models and estimation methods used in the financial modelling and valuation of financial derivatives.
MSc Hanxue Yang defends her doctoral dissertation at TUT on 13 November. Photo: Jun Hu.
MSc Hanxue Yang defends her doctoral dissertation at TUT on 13 November. Photo: Jun Hu.

A financial model plays a key role in the valuation and risk management of financial derivatives. Recently, many model specifications have been proposed, including the specification of non-affine variance dynamics and the inclusion of Lévy jumps. However, the complexity introduced by further model specifications leads to poor probabilistic properties, and hence most popular estimation methods are not applicable. The Bayesian Markov Chain Monte Carlo (MCMC) method is among the few that work.

The dissertation of MSc Hanxue Yang aims to identify the specifications that make the model efficient and robust in goodness of fit and option pricing and to examine the performance of different estimation methods in dealing with complex models on the basis of simulation and empirical studies.

The results show that a parsimonious model with infinite-activity Normal Inverse Gaussian jumps in returns and non-affine variance dynamics is particularly competitive. Moreover, despite the high computation cost, the Fast Universal Self-tuned Sampler algorithms are efficient in generating virtually independent samples and achieving the fastest convergence with a fixed number of MCMC runs, and their performance is stable regardless of model specifications, whereas the computationally cheap and efficient Particle MCMC methods are more vulnerable to model specifications.

Public defense of a doctoral dissertation on Friday, 13 November

The doctoral dissertation of MSc Hanxue Yang in the field of financial mathematics titled “Markov Chain Monte Carlo Estimation of Stochastic Volatility Models with Finite and Infinite Activity Lévy Jumps: Evidence for Efficient Models and Algorithms” 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 12:00 on Friday, 13 November 2015.

The opponents will be Professor Heikki Haario (Lappeenranta University of Technology) and Professor Andreas Kaeck (University of Sussex). Professor Juho Kanniainen from the Department of Industrial Management at TUT will act as Chairman.

Hanxue Yang (27), from Shanghai, China, works as a Marie Curie researcher on the HPCFinance project (www.hpcfinance.eu) at the Department of Industrial Management at TUT.

Further information:

Hanxue Yang, tel. 050 301 0118, hanxue.yang@tut.fi

The dissertation is available online at: http://dspace.cc.tut.fi/dpub/handle/123456789/23454

News submitted by: Anna Naukkarinen
Keywords: science and research, image and communications, hanxue yang, doctoral dissertation